U.S. patent application number 14/467272 was filed with the patent office on 2016-02-25 for rapid anomaly detection (rand).
The applicant listed for this patent is Bradley A. Flanders, Ian S. Robinson, Anthony M. Sommese. Invention is credited to Bradley A. Flanders, Ian S. Robinson, Anthony M. Sommese.
Application Number | 20160055654 14/467272 |
Document ID | / |
Family ID | 55314702 |
Filed Date | 2016-02-25 |
United States Patent
Application |
20160055654 |
Kind Code |
A1 |
Flanders; Bradley A. ; et
al. |
February 25, 2016 |
RAPID ANOMALY DETECTION (RAND)
Abstract
A rapid anomaly detection approach with corresponding method and
system to detect anomalies in scene pixels making up a
hyperspectral scene, efficiently, is presented. The approach
includes tailoring an approximation of an anomaly score for each
scene pixel, individually, based on an "intermediate anomaly
score." The intermediate score is computed using a portion of the
terms used to compute the anomaly score. Scene pixels with low
intermediate anomaly scores are removed from further processing.
The remaining scene pixels are further processed, including
computing anomaly scores to detect anomalies in these pixels.
Advantageously, examples of the RAND approach process a few terms
of all scene pixels, eliminate most scene pixels, and calculate
more terms on high anomaly scoring scene pixels as needed.
Inventors: |
Flanders; Bradley A.;
(Whittier, CA) ; Sommese; Anthony M.; (Northport,
NY) ; Robinson; Ian S.; (Redondo Beach, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Flanders; Bradley A.
Sommese; Anthony M.
Robinson; Ian S. |
Whittier
Northport
Redondo Beach |
CA
NY
CA |
US
US
US |
|
|
Family ID: |
55314702 |
Appl. No.: |
14/467272 |
Filed: |
August 25, 2014 |
Current U.S.
Class: |
382/162 |
Current CPC
Class: |
G06K 9/6214 20130101;
G06T 7/174 20170101; G06T 2207/10036 20130101; G06T 7/90 20170101;
G06T 2207/20016 20130101; G06T 7/136 20170101; G06T 2207/10004
20130101; G06T 2207/10024 20130101; G06K 9/0063 20130101; G06T 7/11
20170101 |
International
Class: |
G06T 7/40 20060101
G06T007/40 |
Claims
1. A method for detecting an anomaly in a scene pixel of a
hyperspectral scene, the anomaly being a spectrum different than
spectra common to a plurality of scene pixels of the hyperspectral
scene, the method comprising: in an anomaly detector provided with
hyperspectral imaging data for a plurality of scene pixels of a
hyperspectral scene and provided with an anomaly equation for
detecting an anomaly in a scene pixel without using an a priori
spectral reference, and the anomaly equation including a set of
terms sorted by descending importance: for each scene pixel in the
plurality of scene pixels, computing an intermediate anomaly score
based on a portion of the set of terms, the remaining terms of the
set are not used to compute the intermediate anomaly score and are
of less importance than the terms included in the portion;
comparing the intermediate anomaly scores to a threshold;
identifying scene pixels having intermediate anomaly scores less
than the threshold as empty scene pixels that do not include
anomalies; eliminating the empty scene pixels from the plurality of
scene pixels; updating the anomaly equation to include one or more
of the unused terms to form an updated anomaly equation if the
number of eliminated scene pixels is less than a specified fraction
of a total number of scene pixels and a number of computed
intermediate anomaly scores is less than a counter; for each scene
pixel remaining in the plurality of scene pixels, computing an
updated intermediate anomaly score using the updated anomaly
equation; comparing the updated intermediate anomaly scores to an
updated threshold, the updated threshold being greater in value
than the threshold; identifying scene pixels having updated
intermediate anomaly scores less than the updated threshold as
empty scene pixels that do not include anomalies; eliminating empty
scene pixels from the remaining scene pixels; for each scene pixel
remaining in the plurality of scene pixels, computing a full
dimension anomaly score based on the set of terms of the anomaly
equation if the number of eliminated scene pixels is greater than
or equal to the specified fraction of the total number of scene
pixels or the number of intermediate anomaly scores computed is
greater than or equal to the counter; and declaring which scene
pixels include anomalies based on comparisons of the computed full
dimension anomaly scores to a full dimension anomaly score
threshold, the full dimension anomaly score threshold being greater
in value than the threshold and the updated threshold.
2. The method of claim 1 wherein the hyperspectral imaging data
includes M principal components of the hyperspectral scene; wherein
computing the intermediate anomaly score includes computing the
intermediate anomaly score from a subset of the M principal
components, the remaining principal components are not used to
compute the intermediate anomaly score; wherein updating the
anomaly equation includes updating the anomaly equation to include
one or more of the unused principal components to form the updated
anomaly equation; and wherein computing the full dimension anomaly
score includes computing the full dimension anomaly score based on
the M principal components.
3. The method of claim 1 wherein the hyperspectral imaging data
includes a plurality of average bands, which are derived by
averaging the HSI spectrum of each scene pixel in the plurality of
scene pixels; wherein computing the intermediate anomaly score
includes computing the intermediate anomaly score from a subset of
the plurality of average bands, the remaining average bands are not
used to compute the intermediate anomaly score; wherein updating
the anomaly equation includes updating the anomaly equation to
include one or more of the average bands to form the updated
anomaly equation; and wherein computing the full dimension anomaly
score includes computing the full dimension anomaly score based on
the plurality of average bands.
4. The method of claim 1 wherein the hyperspectral imaging data
includes N coefficients for each scene pixel, which are derived by
unmixing N basis vectors from a spectral vector associated each
scene pixel; wherein computing the intermediate anomaly score
includes computing the intermediate anomaly score from a subset of
the N coefficients, the remaining coefficients are not used to
compute the intermediate anomaly score; wherein updating the
anomaly equation includes updating the anomaly equation to include
one or more of the unused coefficients to form the updated anomaly
equation; and wherein computing the full dimension anomaly score
includes computing the full dimension anomaly score based on the N
coefficients.
5. The method of claim 1 further comprising revising the
intermediate anomaly score to form a revised anomaly score and
wherein comparing includes comparing the revised anomaly score to
the threshold.
6. The method of claim 5 wherein revising further includes
approximating the unevaluated terms of the anomaly equation to form
a score corresponding with unevaluated terms; adding the score to
the intermediate anomaly score to form the revised anomaly score;
and comparing the revised anomaly score to a threshold that changes
"slightly with each iteration to account for decreasing uncertainty
in the score rather than changing to account for an increasing
score as in the basic approach"
7. The method of claim 5 wherein revising further includes
calculating an upper bound of the unevaluated terms of the anomaly
equation; adding the upper bound to the intermediate anomaly score
to form the revised anomaly score; and comparing the revised
anomaly score to a threshold used to compare a full dimension
anomaly score.
8. A system for detecting an anomaly in a scene pixel of a
hyperspectral scene, the anomaly being a spectrum different than
spectra common to a plurality of scene pixels of the hyperspectral
scene, the system comprising: a memory having computer executable
instructions thereupon; at least one interface receiving a
hyperspectral imaging data for a plurality of scene pixels of a
hyperspectral scene and an anomaly equation for detecting an
anomaly in a scene pixel without using an a priori spectral
reference, the anomaly equation including a set of terms sorted by
descending importance; and an anomaly detector coupled to the
memory and the at least one interface, the computer executable
instructions when executed by the anomaly detector cause the
anomaly detector to: compute an intermediate anomaly score for each
scene pixel in the plurality of scene pixels based on a portion of
the set of terms, the remaining terms of the set are not used to
compute the intermediate anomaly score and are of less importance
than the terms included in the portion; compare the intermediate
anomaly scores to a threshold; identify scene pixels having
intermediate anomaly scores less than the threshold as empty scene
pixels that do not include anomalies; eliminate the empty scene
pixels from the plurality of scene pixels; update the anomaly
equation to include one or more of the unused terms to form an
updated anomaly equation if the number of eliminated scene pixels
is less than a specified fraction of a total number of scene pixels
and a number of computed intermediate anomaly scores is less than a
counter; compute an updated intermediate anomaly score for each
scene pixel remaining in the plurality of scene pixels using the
updated anomaly equation; compare the updated intermediate anomaly
scores to an updated threshold, the updated threshold being greater
in value than the threshold; identify scene pixels having updated
intermediate anomaly scores less than the updated threshold as
empty scene pixels that do not include anomalies; eliminate empty
scene pixels from the remaining scene pixels; for each scene pixel
remaining in the plurality of scene pixels, compute a full
dimension anomaly score based on the set of terms of the anomaly
equation if the number of eliminated scene pixels is greater than
or equal to the specified fraction of the total number of scene
pixels or the number of intermediate anomaly scores computed is
greater than or equal to the counter; and declare which scene
pixels include anomalies based on comparisons of the computed full
dimension anomaly scores to a full dimension anomaly score
threshold, the full dimension anomaly score threshold being greater
in value than the threshold and the updated threshold.
9. A tangible non-transitory computer-readable storage medium
having computer readable instructions stored therein for detecting
an anomaly in a scene pixel of a hyperspectral scene, the anomaly
being a spectrum different than spectra common to a plurality of
scene pixels of the hyperspectral scene, which when executed by one
or more processors provided with a hyperspectral imaging data for a
plurality of scene pixels of a hyperspectral scene and an anomaly
equation for detecting an anomaly in a scene pixel without using an
a priori spectral reference, the anomaly equation including a set
of terms sorted by descending importance, cause the one or more
processors to: compute an intermediate anomaly score for each scene
pixel in the plurality of scene pixels based on a portion of the
set of terms, the remaining terms of the set are not used to
compute the intermediate anomaly score and are of less importance
than the terms included in the portion; compare the intermediate
anomaly scores to a threshold; identify scene pixels having
intermediate anomaly scores less than the threshold as empty scene
pixels that do not include anomalies; eliminate the empty scene
pixels from the plurality of scene pixels; update the anomaly
equation to include one or more of the unused terms to form an
updated anomaly equation if the number of eliminated scene pixels
is less than a specified fraction of a total number of scene pixels
and a number of computed intermediate anomaly scores is less than a
counter; compute an updated intermediate anomaly score for each
scene pixel remaining in the plurality of scene pixels using the
updated anomaly equation; compare the updated intermediate anomaly
scores to an updated threshold, the updated threshold being greater
in value than the threshold; identify scene pixels having updated
intermediate anomaly scores less than the updated threshold as
empty scene pixels that do not include anomalies; eliminate empty
scene pixels from the remaining scene pixels; for each scene pixel
remaining in the plurality of scene pixels, compute a full
dimension anomaly score based on the set of terms of the anomaly
equation if the number of eliminated scene pixels is greater than
or equal to the specified fraction of the total number of scene
pixels or the number of intermediate anomaly scores computed is
greater than or equal to the counter; and declare which scene
pixels include anomalies based on comparisons of the computed full
dimension anomaly scores to a full dimension anomaly score
threshold, the full dimension anomaly score threshold being greater
in value than the threshold and the updated threshold.
Description
BACKGROUND
[0001] In many conventional image processing scenarios comprising
hyperspectral imaging (HSI) systems, hyperspectral sensors collect
data of an image or scene from one spatial line and disperse the
spectrum across a perpendicular direction of the focal plane of the
optics receiving the image. Thus a focal plane pixel measures the
intensity of a given spot on the ground in a specific waveband. A
complete HSI cube scene is formed by scanning this spatial line
across the scene that is imaged. The complete HSI cube may be
analyzed as a measurement of the spectrum, the intensity in many
wavebands, for a spatial or scene pixel. This scene pixel
represents a given spot on the ground in a cross-scan direction for
one of the lines at a given time in the scan direction. These
spectra are analyzed to detect spectral anomalies.
SUMMARY
[0002] In accordance with an example, a method for detecting an
anomaly in a scene pixel of a hyperspectral scene is provided,
which enables anomaly detection with fewer computations. The
anomaly being a spectrum different than spectra common to a
plurality of scene pixels of the hyperspectral scene. The method
includes, in an anomaly detector provided with hyperspectral
imaging data for a plurality of scene pixels of a hyperspectral
scene and provided with an anomaly equation for detecting an
anomaly in a scene pixel without using an a priori spectral
reference, and the anomaly equation including a set of terms sorted
by descending importance, for each scene pixel in the plurality of
scene pixels, computing an intermediate anomaly score based on a
portion of the set of terms, the remaining terms of the set are not
used to compute the intermediate anomaly score and are of less
importance than the terms included in the portion. The method
further includes comparing the intermediate anomaly scores to a
threshold. The method further includes identifying scene pixels
having intermediate anomaly scores less than the threshold as empty
scene pixels that do not include anomalies. The method further
includes eliminating the empty scene pixels from the plurality of
scene pixels. The method further includes updating the anomaly
equation to include one or more of the unused terms to form an
updated anomaly equation if the number of eliminated scene pixels
is less than a specified fraction of a total number of scene pixels
and a number of computed intermediate anomaly scores is less than a
counter. The method further includes for each scene pixel remaining
in the plurality of scene pixels, computing an updated intermediate
anomaly score using the updated anomaly equation. The method
further includes comparing the updated intermediate anomaly scores
to an updated threshold, the updated threshold being greater in
value than the previous threshold. The method further includes
identifying scene pixels having updated intermediate anomaly scores
less than the updated threshold as empty scene pixels that do not
include anomalies. The method further includes eliminating empty
scene pixels from the remaining scene pixels. The method further
includes for each scene pixel remaining in the plurality of scene
pixels, computing a full dimension anomaly score based on the set
of terms of the anomaly equation if the number of eliminated scene
pixels is greater than or equal to the specified fraction of the
total number of scene pixels or the number of intermediate anomaly
scores computed is greater than or equal to the counter. The method
further includes declaring which scene pixels include anomalies
based on comparisons of the computed full dimension anomaly scores
to a full dimension anomaly score threshold, the full dimension
anomaly score threshold being greater in value than the threshold
and the updated threshold.
[0003] In accordance with another example, a system for detecting
an anomaly in a scene pixel of a hyperspectral scene is provided.
The anomaly is a spectrum different than spectra common to a
plurality of scene pixels of the hyperspectral scene. The system
includes memory having computer executable instructions thereupon
and at least one interface receiving a hyperspectral imaging data
for a plurality of scene pixels of a hyperspectral scene and an
anomaly equation for detecting an anomaly in a scene pixel without
using an a priori spectral reference, the anomaly equation
including a set of terms sorted by descending importance. The
system further includes an anomaly detector coupled to the memory
and the at least one interface coupled to the memory and the at
least one interface. The computer executable instructions when
executed by the anomaly detector cause the anomaly detector to
compute an intermediate anomaly score for each scene pixel in the
plurality of scene pixels based on a portion of the set of terms,
the remaining terms of the set are not used to compute the
intermediate anomaly score and are of less importance than the
terms included in the portion. The anomaly detector further caused
to compare the intermediate anomaly scores to a threshold. The
anomaly detector further caused to identify scene pixels having
intermediate anomaly scores less than the threshold as empty scene
pixels that do not include anomalies. The anomaly detector further
caused to eliminate the empty scene pixels from the plurality of
scene pixels. The anomaly detector further caused to update the
anomaly equation to include one or more of the unused terms to form
an updated anomaly equation if the number of eliminated scene
pixels is less than a specified fraction of a total number of scene
pixels and a number of computed intermediate anomaly scores is less
than a counter. The anomaly detector further caused to compute an
updated intermediate anomaly score for each scene pixel remaining
in the plurality of scene pixels using the updated anomaly
equation. The anomaly detector further caused to compare the
updated intermediate anomaly scores to an updated threshold, the
updated threshold being greater in value than the previous
threshold. The anomaly detector further caused to identify scene
pixels having updated intermediate anomaly scores less than the
updated threshold as empty scene pixels that do not include
anomalies. The anomaly detector further caused to eliminate empty
scene pixels from the remaining scene pixels. The anomaly detector
further caused to, for each scene pixel remaining in the plurality
of scene pixels, compute a full dimension anomaly score based on
the set of terms of the anomaly equation if the number of
eliminated scene pixels is greater than or equal to the specified
fraction of the total number of scene pixels or the number of
intermediate anomaly scores computed is greater than or equal to
the counter. The anomaly detector further caused to declare which
scene pixels include anomalies based on comparisons of the computed
full dimension anomaly scores to a full dimension anomaly score
threshold, the full dimension anomaly score threshold being greater
in value than the threshold and the updated threshold.
[0004] In accordance with yet another example, a tangible
computer-readable storage medium having computer readable
instructions stored therein for detecting an anomaly in a scene
pixel of a hyperspectral scene is provided. The anomaly is a
spectrum different than spectra common to a plurality of scene
pixels of the hyperspectral scene The computer readable
instructions when executed by one or more processors provided with
a hyperspectral imaging data for a plurality of scene pixels of a
hyperspectral scene and an anomaly equation for detecting an
anomaly in a scene pixel without using an a priori spectral
reference, the anomaly equation including a set of terms sorted by
descending importance, cause the one or more processors to compute
an intermediate anomaly score for each scene pixel in the plurality
of scene pixels based on a portion of the set of terms, the
remaining terms of the set are not used to compute the intermediate
anomaly score and are of less importance than the terms included in
the portion. The one or more processors further caused to compare
the intermediate anomaly scores to a threshold. The one or more
processors further caused to identify scene pixels having
intermediate anomaly scores less than the threshold as empty scene
pixels that do not include anomalies. The one or more processors
further caused to eliminate empty scene pixels from the remaining
scene pixels. The one or more processors further caused to update
the anomaly equation to include one or more of the unused terms to
form an updated anomaly equation if the number of eliminated scene
pixels is less than a specified fraction of a total number of scene
pixels and a number of computed intermediate anomaly scores is less
than a counter. The one or more processors further caused to
compute an updated intermediate anomaly score for each scene pixel
remaining in the plurality of scene pixels using the updated
anomaly equation. The one or more processors further caused to
compare the updated intermediate anomaly scores to an updated
threshold, the updated threshold being greater in value than the
previous threshold. The one or more processors further caused to
identify scene pixels having updated intermediate anomaly scores
less than the updated threshold as empty scene pixels that do not
include anomalies. The one or more processors further caused to
eliminate empty scene pixels from the remaining scene pixels. The
one or more processors further caused to for each scene pixel
remaining in the plurality of scene pixels, compute a full
dimension anomaly score based on the set of terms of the anomaly
equation if the number of eliminated scene pixels is greater than
or equal to the specified fraction of the total number of scene
pixels or the number of intermediate anomaly scores computed is
greater than or equal to the counter. The one or more processors
further caused to declare which scene pixels include anomalies
based on comparisons of the computed full dimension anomaly scores
to a full dimension anomaly score threshold, the full dimension
anomaly score threshold being greater in value than the threshold
and the updated threshold.
[0005] In some examples, any of the aspects above can include one
or more of the following features.
[0006] In other examples of the method, the hyperspectral imaging
data includes M principal components of the hyperspectral scene.
The method includes computing the intermediate anomaly score from a
subset of the M principal components, the remaining principal
components are not used to compute the intermediate anomaly score,
updating the anomaly equation includes updating the anomaly
equation to include one or more of the unused principal components
to form the updated anomaly equation, and computing the full
dimension anomaly score includes computing the full dimension
anomaly score based on the M principal components.
[0007] In some examples of the method, the hyperspectral imaging
data includes a plurality of average bands, which are derived by
averaging the HSI spectrum of each scene pixel in the plurality of
scene pixels. The method includes computing the intermediate
anomaly score from a subset of the plurality of average bands, the
remaining average bands are not used to compute the intermediate
anomaly score, updating the anomaly equation to include one or more
of the average bands to form the updated anomaly equation, and
computing the full dimension anomaly score based on the plurality
of average bands.
[0008] In some examples of the method, the hyperspectral imaging
data includes N coefficients for each scene pixel, which are
derived by unmixing N basis vectors from a spectral vector
associated each scene pixel. The method includes computing the
intermediate anomaly score from a subset of the N coefficients, the
remaining coefficients are not used to compute the intermediate
anomaly score, updating the anomaly equation to include one or more
of the unused coefficients to form the updated anomaly equation,
and computing the full dimension anomaly score based on the N
coefficients.
[0009] These and other features and characteristics, as well as the
methods of operation and functions of the related elements of
structure and the combination of parts and economies of
manufacture, will become more apparent upon consideration of the
following description and the appended claims with reference to the
accompanying drawings, all of which form a part of this
specification, wherein like reference numerals designate
corresponding parts in the various figures. It is to be expressly
understood, however, that the drawings are for the purpose of
illustration and description only and are not intended as a
definition of the limits of claims. As used in the specification
and in the claims, the singular form of "a", "an", and "the"
include plural referents unless the context clearly dictates
otherwise.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The foregoing and other objects, features and advantages
will be apparent from the following more particular description of
the examples, as illustrated in the accompanying drawings in which
like reference characters refer to the same parts throughout the
different views. The drawings are not necessarily to scale,
emphasis instead being placed upon illustrating the principles of
the examples.
[0011] FIG. 1 is an illustration of an example imaging system with
an anomaly detector.
[0012] FIG. 2 is an illustration of an example representation of
one or more images of a hyperspectral scene including a plurality
of scene pixels.
[0013] FIG. 3 is a functional block diagram of an example of the
anomaly detector.
DETAILED DESCRIPTION
[0014] In the description that follows, like components have been
given the same reference numerals, regardless of whether they are
shown in different examples. To illustrate an example(s) of the
present disclosure in a clear and concise manner, the drawings may
not necessarily be to scale and certain features may be shown in
somewhat schematic form. Features that are described and/or
illustrated with respect to one example may be used in the same way
or in a similar way in one or more other examples and/or in
combination with or instead of the features of the other
examples.
[0015] Depicted in FIG. 1 is an example of imaging system 102 that
is configured to process images and to detect
materials/targets/anomalies in backgrounds/scenes. By way of
example only, imaging system 102 may be a hyperspectral imaging
(HSI) system. The term "hyperspectral" refers to imaging narrow
spectral bands over a continuous spectral range, and producing the
spectra of all scene pixels in a hyperspectral scene (e.g., scene
106). Imaging system 102 may be stationary or mobile, airborne or
land based (e.g., on an elevated land structure or building), or
may be on an aircraft or a satellite. As shown, imaging system 102
may incorporate image processor 100, and may be coupled to or
otherwise contained within remote imaging system 104. Remote
imaging system 104 may be of any suitable construction or
configuration, including but not limited to comprising a satellite,
an aerial surveillance system, or any other system that can capture
images. Additionally, remote imaging system 104 may be stationary
or mobile. In an example, imaging system 102 and remote imaging
system 104 may be configured to capture one or more images of a
particular scene 106 corresponding to a geographical area (e.g., a
ground terrain).
[0016] In an example, remote imaging system 104 may be configured
to use imaging system 102 to capture hyperspectral image(s) of
scene 106 that are provided as input hyperspectral image (HSI)
scene to image processor 100. In an example, hyperspectral imaging
system 102 may include one or more scan mirrors 110, or may include
other optics arranged to receive light 108 reflected from one or
more ground resolution cells. Light 108 reflected from one or more
ground resolution cells, and generally the entire scene 106, may be
used by image processor 100 to determine an input reflectivity of
input HSI scene. Input HSI scene may be a part of scene 106, or may
be the entire scene 106 depending upon specific target detection
goals. In an example, scan mirrors 110 or the other optics may then
direct light 108 through dispersing element 112, which may be
arranged to separate light 108 into various different wavelengths
(i.e., a spectra). After being separated into the various different
wavelengths, light 108 may then be directed to one or more imaging
optics 114, which may focus the various wavelengths onto a focal
plane of detector array 116. As such, detector array 116 may
capture hyperspectral data across the spectrum of wavelengths,
thereby generating a data set corresponding to a hyperspectral
image of scene 106.
[0017] FIG. 2 provides an exemplary representation of hyperspectral
image 200, which may generally include a plurality of images
200i-n, which may be acquired in a substantially simultaneous
manner across various different wavelength (.lamda.) bands. As
shown in FIG. 2, the hyperspectral image 200 may include a
plurality of scene pixels 202 arranged according to an x-y
coordinate system, although it will be apparent that alternate
coordinate systems may be employed in various circumstances. (A
scene pixel can also be referred to as "spatial pixel" or simply
"pixel," all of which may be used interchangeably herein.) In one
example, each respective scene pixel 202 may have a spectral vector
204 associated therewith, wherein the spectral vector 204 may
contain one or more spectral measurements representing energy
associated with the respective scene pixel 202. For example, FIG. 2
further illustrates an enlarged section 206 representing a
particular row X' of scene pixels 202a-n from one of the plurality
of images 200i-n, wherein each of the various scene pixels 202a-n
in the row X' may have a spectral vector 204a-n representing the
energy associated with the respective pixel 202.
[0018] A key function of a HSI system is detecting anomalies in
scene pixels making up a hyperspectral scene. An anomaly is
spectrum that is not known beforehand but is different than spectra
common to the scene pixels. Detecting an anomaly is contrasted with
detecting a target which requires the unique spectrum or
"signature" associated with the target to be known beforehand.
Anomaly detection is a critical to the operation of a HSI system in
a denied territory. Anomaly detection provides the capability for
the system to learn new targets, as scene pixels detected,
initially, as anomalies may become targets whose spectrum is known
on a subsequent flight.
[0019] A common form of anomaly detector is the Reed-Xioli or "RX"
anomaly detection filter, RX=(x-.mu.).sup.T Cov.sup.-1(x-.mu.),
where x is a pixel's spectral vector and X is the scene spectral
mean. The RX anomaly detection filter is effective but requires a
lot of computation for each scene pixel. Computation of the RX
anomaly detection filter for all scene pixels requires P*D 2
operations, where P is the number scene pixels and D is the number
of dimensions or bands. A hyperspectral scene typically includes
1E6 scene pixels. A HSI sensor typically collects 200-400 narrow
spectral bands over a given sensing regime (e.g., visible and near
infra-red (VNIR)/short wave infrared (SWIR) and long wave infrared
(LWIR)). Therefore, anomaly detection is ordinarily a very
computation intensive process.
[0020] As shown, the imaging system 102 includes an anomaly
detector 300 for detecting anomalies in scene pixels making up a
hyperspectral scene. However, it may not be practical for the
anomaly detector 300 to perform so many operations, as described
above, to detect an anomaly, especially when the anomaly detector
300 is located "on-board" the remote imaging system 104. It may
also not be practical to perform this many operations "on-ground."
A ground station may have to process up to several terabytes of
data per day.
[0021] A rapid anomaly detection or RAND approach is presented to
efficiently detect anomalies in scene pixels making up a
hyperspectral scene. The approach includes tailoring an
approximation of an anomaly score for each scene pixel,
individually, based on an "intermediate anomaly score." The
intermediate anomaly score is computed using a portion of the terms
used to compute an anomaly score, herein referred to as a "full
dimension anomaly score" The anomaly score computed by the RX
anomaly detection filter described above is an example of a full
dimension anomaly score.
[0022] The approach further includes focusing the tailoring to
eliminate scene pixels with low anomaly scores from further
processing. Because most scene pixels in a hyperspectral scene have
low anomaly scores, with only few having high anomaly scores, a
large number of scene pixels can be removed from processing making
further processing more efficient. Studies have shown that fewer
terms in an anomaly scoring expression (e.g., the RX anomaly
detection filter equation described above) are needed to identify
low anomaly scoring scene pixels than are needed to identify high
anomaly scoring pixels. Advantageously, examples of the RAND
approach process a few terms of all scene pixels, eliminate most
scene pixels, and calculate more terms on high anomaly scoring
scene pixels (e.g., the remaining scene pixels) as needed.
[0023] In general, examples of the RAND approach divide the anomaly
detection problem into two parts. First, an intermediate anomaly
filter (e.g., an anomaly equation with a few terms) is applied to
all scene pixels in a plurality to produce an intermediate anomaly
score for each scene pixel. Several ways for implementing this
intermediate stage are described below. The intermediate anomaly
score is different from the full dimension anomaly score. The full
dimension anomaly score is used to detect a scene pixel with an
anomaly. In contrast, the intermediate anomaly score is used to
detect a scene pixel without an anomaly. Scene pixels having
intermediate anomaly scores below a threshold are declared "empty
scene pixels" and are not processed further.
[0024] To aid in identifying empty scene pixels, in some examples
of the RAND approach, an optional step is included for estimating
the contribution of the un-used terms in the full dimension anomaly
score with few computations. This estimation could consist of an
upper bound of a full dimension anomaly score or an estimate of the
full dimension anomaly score. If an estimate of the full dimension
anomaly score is calculated, scene pixels that are close to the
threshold but have estimated full dimension scores below a second,
higher threshold are also declared empty scene pixels. If an upper
bound of the full dimension score is calculated, the resulting
score can be compared to the threshold used by full dimension
anomaly processing without risk of missing any anomalies.
[0025] Having identified which scene pixels of the hyperspectral
scene are empty scene pixels, examples of the RAND approach apply
one or more full dimension anomaly detection filters (e.g., the RX
anomaly detection filter described above) to the remaining pixels.
The results of the filter or combination of filters are thresholded
to declare whether an anomaly is present or not in a scene pixel.
Operating on a few percent or less of the scene pixels (e.g.,
0.1%-2%) with full dimension and operating on the bulk of scene
pixels with far less processing using many fewer dimensions (e.g.
10-100.times. fewer dimensions) saves 50-1000.times. of the
processing steps, depending on the number of scene pixels
processed.
[0026] FIG. 3 shows an example of the anomaly detector 300 for
implementing examples of the RAND approach. The anomaly detector
300 includes an intermediate anomaly scoring module 305, an
optional score revising module 310, an identifying module 315, and
a full dimension anomaly scoring module 320 communicatively coupled
to one another as shown.
[0027] The anomaly detector 300 is provided with hyperspectral
imaging data 301 for a plurality of scene pixels in a hyperspectral
scene. The hyperspectral imaging data 301 includes, for example, a
HSI data cube, and eigenvectors and eigenvalues associated with the
input date cube. Some examples of the RAND approach calculate the
eigenvectors and eigenvalues from the data cube if they are not
provided.
[0028] The intermediate anomaly scoring module 305 computes an
intermediate anomaly score (RX.sub.M) for each scene pixel in the
plurality of scene pixels. The intermediate anomaly scoring module
305 uses the first few terms of an anomaly score (anomaly equation)
comprising the first few eigenvectors and eigenvalues (e.g., 3 to
5) of the input hyperspectral imaging data 301 to compute the
intermediate anomaly score 306.
[0029] The identifying module 315 compares the intermediate anomaly
scores to a threshold appropriate for the first pass intermediate
score. (Selecting a suitable threshold is discussed later in
conjunction with the score revising module 310.) The identifying
module 315 declares scene pixels below the threshold to be not of
interest. These pixels are declared empty scene pixels that do not
include anomalies and are flagged for no further processing.
[0030] A convenient example of the identifying module 315
eliminates empty scene pixels from the plurality of scene pixels.
It is noted, that the value of the threshold for comparing the
intermediate anomaly score is smaller than the value of a threshold
used for comparing the full dimension anomaly score. The threshold
is used to identify, and in some examples, eliminate low anomaly
scoring pixels rather than identifying high anomaly scoring
pixels.
[0031] Scene pixels above the threshold (e.g., scene pixels
remaining in the plurality of scene pixels) potentially include
anomalies, and are further processed by the anomaly detector 300.
For sake of discussion and for readability, scene pixels that may
include anomalies are called "potential anomalies." If a number of
potential anomalies is sufficiently small (e.g., as determined by a
set number or a set fraction of the plurality of scene pixels are
identified as empty scene pixels), the full dimension anomaly
scoring module 320 calculates the remaining terms of the anomaly
score (anomaly equation) to generate a full dimension anomaly score
for each of the remaining scene pixels. If the number of potential
anomalies is large (e.g., as determined by a set number or a set
fraction of the plurality of scene pixels are identified as empty
scene pixels), the intermediate anomaly scoring module 305 adds a
few more of the remaining terms of the anomaly score (anomaly
equation) to compute an updated intermediate anomaly score for
those remaining scene pixels.
[0032] In the case when a few more terms are calculated for
potential anomalies, the updated intermediate anomaly score is
compared to an updated threshold appropriate for a second pass
intermediate score. The identifying module 315 declares scene
pixels below the updated threshold to be not of interest. These
pixels are declared empty scene pixels that do not include
anomalies and are flagged for no further processing.
[0033] A convenient example of the identifying module 315
eliminates empty scene pixels from the plurality of scene pixels.
It is noted, that the value of the updated threshold for comparing
the updated intermediate anomaly score is higher than the value of
the threshold used in the prior iteration but lower than the
threshold value used for comparing the full dimension anomaly
score.
[0034] The foregoing iterative loop continues until sufficiently
few scene pixels remain or the anomaly detector 300 executes a
maximum number of loops. A counter may be provided (e.g., as an
operator defined parameter) or incremented to determine whether
sufficiently few scene pixel remain or whether a maximum number of
loops is obtained.
[0035] The full dimension anomaly scoring module 320 calculates the
remaining terms of the anomaly score (anomaly equation) to generate
a full dimension anomaly score for each scene pixel still flagged
as potential anomalies (e.g., scene pixels remaining in the
plurality). The full dimension anomaly scoring module 320 compares
the full dimension anomaly score with a full dimension threshold.
Scene pixels with scores above the full dimension threshold are
considered to include anomalies and the anomaly detector 300
returns these scene pixels or an indicator (e.g., detection 321)
that anomalies are detected in these scene pixels
[0036] The RAND approach contemplates several ways of computing the
intermediate anomaly score. In a convenient example of the RAND
approach, provided M eigenvector coefficients computed for each
scene pixel, the intermediate anomaly scoring module 305 computes
the intermediate anomaly score from the largest (top) M (e.g. 3-5)
principal components of the hyperspectral scene. These are the M
eigenvectors of the Cov with the largest corresponding eigenvalues
of the D eigenvector/eigenvalue pairs computed for the Cov. The
computation requires P*M 2 number of operations.
[0037] If the M eigenvector coefficients are not provided, they may
be calculated from the HSI datacube, requiring P*M*D operations.
The coefficients are calculated by unmixing the principal
components from each scene pixel and dividing the resulting
unmixing coefficient by the square root of the eigenvalue
corresponding to the eigenvector. The result is each pixel has M
whitened coefficients.
[0038] The intermediate anomaly score is the dot product of the M
coefficients with themselves and is equivalent to the output of the
anomaly score (anomaly equation) for M coefficients. When M is 3,
for example, the foregoing computation requires approximately P*M 2
or 9P operations. The intermediate anomaly score can also be
computed by equation 1 below, in which the full eigenvalue is a
divisor before summing dot product terms. The advantage to this way
of computing the intermediate anomaly score is that enables an
upper bound to be estimated on the full dimension anomaly score and
will be described later in detail.
[0039] Using flight test data, it has been shown that some examples
of the RAND approach separate pixels with targets from empty pixels
with M as low as 3 terms. It has also been show that some examples
of the RAND approach eliminated over 99% of the scene pixels
without any degradation to probability of detection (Pd) and
probability of false alarm (Pfa).
[0040] In another example of the RAND approach, the HSI spectrum of
each pixel has been averaged over multiple adjacent wavebands and
provided as a "multi spectral" data set of average bands (e.g.,
5-10 HSI bands). These average bands are the used to create another
spectral data cube with fewer dimensions. For example, 3 bands
(1-3) are averaged together to form 1 band (the new 1), the next 3
(4-6) are averaged together to form the next band (the new 2),
etc.
[0041] In this example, the RAND approach reduces M by a factor of
3 (K=M/3) which, in turn, reduces the covariance matrix calculation
to K.times.K dimensions, which is faster to calculate than a matrix
of M.times.M dimensions. K principal components are then calculated
from the covariance matrix, which again is faster to calculate than
M principal components. In this example of the RAND approach, the
anomaly equation is "rebuilt" to include K terms. The intermediate
anomaly scoring module 305 uses the first few terms of the K terms
to compute the intermediate anomaly score, as described above.
[0042] In another example of the RAND approach, the HSI spectrum of
each scene pixel has been reduced to N coefficients. The unmixing
of basis vectors is a common approach to reducing the
dimensionality of the incoming data cube. Other approaches are
described in U.S. Pat. No. 8,675,989 OPTIMIZED ORTHONORMAL SYSTEM
AND METHOD FOR REDUCING DIMENSIONALITY OF HYPERSPECTRAL IMAGES,
filed Apr. 13, 2011, which is incorporated herein by reference in
its entirety. These dimension reducing approaches have the
advantage of discovering N basis vectors while simultaneously
reducing each scene pixel to N coefficients and a residual
magnitude, requiring approximately P*N*D operation.
[0043] In this example of the RAND approach, an input data cube is
expressed as N dimensional basis vector coefficients rather than M
dimensional spectral vectors. A N.times.N covariance matrix is
calculated from the input data cube from which N principal
components are computed. The anomaly equation is rebuilt to include
N terms, which is typically 3-10. The intermediate anomaly scoring
module 305 uses the first few terms of the N terms, to compute the
intermediate anomaly score, as described above, requiring roughly
P*N 2 operations.
[0044] Provided D whitened coefficients computed for every scene
pixel and the dot product of the D principal components of each
whitened scene pixel with itself, the full dimension anomaly score
can be calculated. The full dimension anomaly score, RX, can be
written as:
RX = i = 1 D p i 2 .lamda. i ##EQU00001##
[0045] where
[0046] p.sub.i are principal component coefficients
[0047] .lamda..sub.i are eigenvalues
[0048] The intermediate anomaly score, RX.sub.M, contains only the
first M terms
RX M = i = 1 M p i 2 .lamda. i ##EQU00002##
[0049] Turning now to a discussion of thresholds used to compare
against intermediate anomaly scores to identify empty scene pixels.
Care is needed in selecting an appropriate series of thresholds
because the anomaly score of every scene pixel increases as each
term is added to the intermediate anomaly score. The anomaly score
is computed using an anomaly equation that can be expressed as a
sum of terms. All of the terms are positive, so the intermediate
anomaly scoring module 305 computes an intermediate score that is
less than the full (final) dimension anomaly score. Because the
terms are ordered in descending importance the most important terms
generally come first in the anomaly equation. As such, the
intermediate anomaly score calculation for most scene pixels start
with the important terms added, and then progressively less
important terms added.
[0050] However, it is theoretically possible for a scene pixel to
have small values of what are usually important terms and then a
large value of what is usually a less important term. In other
words, a large increase in the anomaly score may occur way down in
a term that is usually small throughout the hyperspectral scene.
This creates a risk of incorrectly declaring that a scene pixel
does not include an anomaly and missing the anomaly.
[0051] In some examples of the RAND approach, the threshold
includes a safety margin. With this safety margin, the threshold is
safely below the full (final) dimension anomaly score threshold,
just in case one of the later (less important) terms that has not
be evaluated yet is unexpectedly large in value. In these examples,
as more terms are evaluated, the threshold increases towards to the
full (final) dimension anomaly score threshold because a smaller
safety margin is needed for unexpectedly large terms.
[0052] Other examples of the RAND approach further reduce the risk
of missing anomalies at the cost of added calculations. In each of
these examples, the score revising module 310 revises an
intermediate anomaly score resulting in a revised intermediate
anomaly score. Instead of comparing the intermediate anomaly score
directly to a threshold, the identifying module 315 compares the
revised intermediate anomaly scores to the threshold.
[0053] One example of the score revising module 310 approximates
all unevaluated terms of the anomaly score to obtain an estimate of
their anomaly score. Adding this estimated term to the intermediate
anomaly score gives an estimate of the full dimension anomaly
score, RX.sub.EST. The identifying module 315 compares the estimate
(RX.sub.EST) to a threshold at each loop to eliminate certain
pixels and select pixels for continued evaluation.
RX EST = RX M + e 2 .lamda. _ ##EQU00003## where ##EQU00003.2##
.lamda. _ = i = M + 1 D .lamda. i ( D - M ) and e 2 = i = M + 1 D p
i 2 = s 2 - i = 1 M p i 2 and s 2 = i = 1 D ( x i - .mu. i ) 2
##EQU00003.3##
[0054] In this example, the threshold changes slightly with each
iteration to account for decreasing uncertainty in the anomaly
score rather than changing to account for an increasing
intermediate anomaly score, as in the case of the safety margin
example described above. The exact sum of the unevaluated terms is
unknown but the sum of the terms is approximately known. As such,
in this example of the RAND approach, a safety margin for a
threshold can be smaller than the safety margin previously
described above. The advantage of a smaller safety margin is that
it will identify more pixels as empty and eliminate more pixels
from additional processing.
[0055] Another example of the score revising module 310 calculates
an upper bound, RX.sub.MAX, for all the unevaluated terms, and thus
an upper bound for the full (final) dimension anomaly score. The
identifying module 315 compares the upper bound (RX.sub.MAX) to the
full dimension anomaly score threshold at each loop to eliminate
certain scene pixels and select scene pixels for continued
evaluation. In this example, the same threshold is used for each
iteration and the maximum score drops as more terms are added to
the intermediate anomaly score and the uncertainty in the score
decreases.
[0056] A key feature of the bounding scheme is based on realizing
that the full (final) dimension anomaly score value differs from
the magnitude of a scene pixel only in weighting each principal
component by its eigenvalue. By assuming a constant eigenvalue, the
eigenvalue can be factored out to give bounds (e.g., the upper
bound, RX.sub.MAX) for the unevaluated anomaly score terms as the
known magnitude of the unevaluated terms divided by the bounding
value of the unevaluated eigenvalues.
[0057] Provided M whitened coefficients computed for every scene
pixel and the dot product of the M principal components of each
whitened scene pixel with itself, the bounds (e.g., the upper
bound, RX.sub.MAX) for the remaining unevaluated components can be
calculated based on the partial magnitude of each scene pixel in
unevaluated components and the maximum or minimum eigenvalue in
unevaluated components. Using the minimum eigenvalue determines a
maximum anomaly score for each scene pixel.
RX = i = 1 D p i 2 .lamda. i ##EQU00004##
[0058] where
[0059] p.sub.i are principal component coefficients
[0060] .lamda..sub.i are eigenvalues
[0061] Rigorous upper and lower bounds can be calculated by
factoring out either the M+1th eigenvalue or the last eigenvalue,
which is the Dth eigenvalue. Note, the decomposition of the Cov
into eigenvectors and eigenvalues automatically sorts eigenvalues
in descending order of importance.
[0062] The upper bound, RX.sub.MAX, is shown in equation below and
is useful to ensure examples of the RAND approach do not eliminate
pixels that could contain anomalies.
RX M + e 2 .lamda. M + 1 < RX < RX M + e 2 .lamda. D
##EQU00005## where ##EQU00005.2## RX M = i = 1 M p i 2 .lamda. i
and e 2 = i = M + 1 D p i 2 = s 2 - i = 1 M p i 2 and s 2 = i = 1 D
( x i - .mu. i ) 2 ##EQU00005.3##
[0063] This example of the RAND approach requires more calculations
primarily because it calculates a rigorous bound (e.g., the upper
bound, RX.sub.MAX) on the maximum anomaly score and requires
evaluation of more terms than the other examples. However, the
benefit of this example is that it is guaranteed to make exactly
the same decisions as the full dimension anomaly score but with
fewer computations. Therefore, this example of the RAND approach
may be favored in critical applications in which missed anomalies
are tolerated less.
[0064] In an another example of the RAND approach provided with
scene pixels, each reduced to N coefficients and a residual
magnitude, an example of the score revising module 310 computes,
per scene pixel, an upper bound (RX.sub.MAX) based on an associated
residual magnitude. The score revising module 310 subtracts the
residual magnitude of each scene pixel by the mean magnitude and
divides the total by the standard deviation of the residual
magnitudes. The result is squared to convert the residual magnitude
of each pixel to a variance term. The sum of the intermediate
anomaly score and the variance term bound the full dimension
anomaly score.
[0065] The above-described systems and methods can be implemented
in digital electronic circuitry, in computer hardware, firmware,
and/or software. The implementation can be as a computer program
product (i.e., a computer program tangibly embodied in an
information carrier medium). The implementation can, for example,
be in a machine-readable storage device for execution by, or to
control the operation of, data processing apparatus. The
implementation can, for example, be a programmable processor, a
computer, and/or multiple computers.
[0066] In one example, a computer program can be written in any
form of programming language, including compiled and/or interpreted
languages, and the computer program can be deployed in any form,
including as a stand-alone program or as a subroutine, element,
and/or other unit suitable for use in a computing environment to
carry out the features and functions of various examples discussed
herein. A computer program can be deployed to be executed on one
computer or on multiple computers at one site.
[0067] Method steps or operations can be performed as processes by
one or more programmable processors executing a computer program to
perform functions of various examples by operating on input data
and generating output. Method steps can also be performed by and an
apparatus can be implemented as special purpose logic circuitry.
The circuitry can, for example, be a field programmable gate array
(FPGA) and/or an application specific integrated circuit (ASIC).
Modules, subroutines, and software agents can refer to portions of
the computer program, the processor, the special circuitry,
software, and/or hardware that implements that functionality.
[0068] The anomaly detector 300 may comprise one or more processors
suitable for the execution of a computer program include, by way of
example, both general and special purpose microprocessors, and any
one or more processors of any kind of digital computer. Generally,
a processor receives instructions and data from a read-only memory
or a random access memory or both. The elements of a computer may
comprise a processor for executing instructions and one or more
memory devices for storing instructions and data. Generally, a
computer can include, can be operatively coupled to receive data
from and/or transfer data to one or more mass storage devices
(e.g., a memory module) for storing data (e.g., magnetic,
magneto-optical disks, or optical disks). The memory may be a
tangible non-transitory computer-readable storage medium having
computer-readable instructions stored therein for processing
images, which when executed by one or more processors (e.g.,
anomaly detector 300) cause the one or more processors to carry out
or implement the features and functionalities of various examples
discussed herein.
[0069] Information carriers suitable for embodying computer program
instructions and data include all forms of non-volatile memory,
including by way of example semiconductor memory devices. The
information carriers can, for example, be EPROM, EEPROM, flash
memory devices, magnetic disks, internal hard disks, removable
disks, magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The
processor and the memory can be supplemented by, and/or
incorporated in special purpose logic circuitry.
[0070] To provide for interaction with a user, the above described
techniques can be implemented on a computing device having a
display device. The display device can, for example, be a cathode
ray tube (CRT) and/or a liquid crystal display (LCD) monitor,
and/or a light emitting diode (LED) monitor. The interaction with a
user can, for example, be a display of information to the user and
a keyboard and a pointing device (e.g., a mouse or a trackball) by
which the user can provide input to the computing device (e.g.,
interact with a user interface element). Other kinds of devices can
be used to provide for interaction with a user. Other devices can,
for example, be feedback provided to the user in any form of
sensory feedback (e.g., visual feedback, auditory feedback, or
tactile feedback). Input from the user can, for example, be
received in any form, including acoustic, speech, and/or tactile
input.
[0071] The above described systems and techniques can be
implemented in a distributed computing system that includes a
back-end component. The back-end component can, for example, be a
data server, a middleware component, and/or an application server.
The above described techniques can be implemented in a distributing
computing system that includes a front-end component. The front-end
component can, for example, be a client computing device having a
graphical user interface, a Web browser through which a user can
interact with an example implementation, and/or other graphical
user interfaces for a transmitting device. The components of the
system can be interconnected by any form or medium of digital data
communication (e.g., a communication network). Examples of
communication networks include a local area network (LAN), a wide
area network (WAN), the Internet, wired networks, and/or wireless
networks.
[0072] The system may be coupled to and/or include clients and
servers. A client and a server are generally remote from each other
and typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computing devices and having a
client-server relationship to each other.
[0073] Communication networks may include packet-based networks,
which can include, for example, the Internet, a carrier internet
protocol (IP) network (e.g., local area network (LAN), wide area
network (WAN), campus area network (CAN), metropolitan area network
(MAN), home area network (HAN)), a private IP network, an IP
private branch exchange (IPBX), a wireless network (e.g., radio
access network (RAN), 802.11 network, 802.16 network, general
packet radio service (GPRS) network, HiperLAN), and/or other
packet-based networks. Circuit-based networks may include, for
example, the public switched telephone network (PSTN), a private
branch exchange (PBX), a wireless network (e.g., RAN, Bluetooth,
code-division multiple access (CDMA) network, time division
multiple access (TDMA) network, global system for mobile
communications (GSM) network), and/or other circuit-based
networks.
[0074] "Comprise," "include," and/or plural forms of each are open
ended and include the listed parts and can include additional parts
that are not listed. "And/or" is open ended and includes one or
more of the listed parts and combinations of the listed parts.
[0075] Although the above disclosure discusses what is currently
considered to be a variety of useful examples, it is to be
understood that such detail is solely for that purpose, and that
the appended claims are not limited to the disclosed examples, but,
on the contrary, are intended to cover modifications and equivalent
arrangements that are within the spirit and scope of the appended
claims.
One skilled in the art will realize the invention may be embodied
in other specific forms without departing from the spirit or
essential characteristics thereof. The foregoing embodiments are
therefore to be considered in all respects illustrative rather than
limiting of the invention described herein. Scope of the invention
is thus indicated by the appended claims, rather than by the
foregoing description, and all changes that come within the meaning
and range of equivalency of the claims are therefore intended to be
embraced therein.
* * * * *