U.S. patent application number 16/885021 was filed with the patent office on 2021-10-14 for method for retrieving atmospheric aerosol based on statistical segmentation.
The applicant listed for this patent is University of Electronic Science and Technology of China. Invention is credited to Yan Chen, Yunping Chen, Yue Yang.
Application Number | 20210318253 16/885021 |
Document ID | / |
Family ID | 1000005866189 |
Filed Date | 2021-10-14 |
United States Patent
Application |
20210318253 |
Kind Code |
A1 |
Chen; Yunping ; et
al. |
October 14, 2021 |
METHOD FOR RETRIEVING ATMOSPHERIC AEROSOL BASED ON STATISTICAL
SEGMENTATION
Abstract
Embodiments include a method for retrieving atmospheric aerosol
based on statistical segmentation. Firstly a multi-band remote
sensing image including an apparent reflectance and an aerosol
optical thickness look-up table corresponding to a retrieval band
is obtained, then pixels are partitioned and screened according to
apparent reflectance segments of a mid-infrared 2.1 micrometer
band. After that the retained pixel sets are further partitioned
and screened according to the apparent reflectance segments of the
mid-infrared 1.6 micrometer band. Finally the obtained pixel sets
are partitioned into two categories according to the pixel number,
one category including pixels having more pixels, the other
including those with less pixels. The category with more pixels is
taken as the reference part for retrieval.
Inventors: |
Chen; Yunping; (Chengdu,
CN) ; Yang; Yue; (Chengdu, CN) ; Chen;
Yan; (Chengdu, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
University of Electronic Science and Technology of China |
Chengdu |
|
CN |
|
|
Family ID: |
1000005866189 |
Appl. No.: |
16/885021 |
Filed: |
May 27, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 21/94 20130101;
G01N 21/3504 20130101 |
International
Class: |
G01N 21/94 20060101
G01N021/94; G01N 21/3504 20060101 G01N021/3504 |
Foreign Application Data
Date |
Code |
Application Number |
May 29, 2019 |
CN |
201910454155.5 |
Claims
1. A method for retrieving atmospheric aerosol based on statistical
segmentation, the method comprising: (S1) acquiring a multi-band
high-resolution remote sensing image of an analyzed area using
satellites, and performing radiometric calibration to obtain a
multi-band remote sensing image comprising an apparent reflectance,
wherein the multi-band remote sensing image comprises a
mid-infrared 1.6 micrometer band and a mid-infrared 2.1 micrometer
band, and selecting a blue/costal band from the multi-band remote
sensing image as an retrieval band; (S2) inputting the multi-band
remote sensing image into an atmospheric radiative transfer model,
obtaining a corresponding aerosol optical thickness look-up table
according to the retrieval band, wherein the look-up table
comprises parameters corresponding to each aerosol optical
thickness value AOT for calculating the apparent reflectance; (S3)
for all pixels in the multi-band remote sensing image, performing
categorization using a statistical segmentation method, wherein the
segmentation method comprises the steps of: (S3.1) partitioning an
apparent reflectance range of the mid-infrared 2.1 micrometer band
into N segments .phi..sub.n n=1, 2, . . . , N, according to a
predetermined interval .lamda..sub.1; (S3.2) based on apparent
reflectances of respective pixels in the multi-band remote sensing
image in the mid-infrared 2.1 micrometer band, allocating the
respective pixels into corresponding segments, to obtain pixel sets
x.sub.n corresponding to respective segments; (S3.3) each pixel set
x.sub.n obtained in step S3.2, in condition that a number of pixels
in the pixel set x.sub.n|x.sub.n|.gtoreq.T.sub.1, T.sub.1 being a
predetermined threshold value, retaining the pixel set, otherwise
deleting the pixel set; marking retained pixel sets as x.sub.n',
n'=1, 2, . . . , N', N' being a number of retained pixel sets;
(S3.4) partitioning an apparent reflectance range of the
mid-infrared 1.6 micrometer band into M segments .gamma..sub.m,
m=1, 2, . . . , M, according to a predetermined interval
.lamda..sub.2; (S3.5) based on apparent reflectances of pixels in
each pixel set x.sub.n' in the mid-infrared 1.6 micrometer band,
allocating respective pixels to a corresponding segment, to obtain
pixel sets y.sub.n',m corresponding to respective segments
.gamma..sub.m in the pixel sets; (3.6) in condition that a number
of pixels in the pixel set y.sub.n',m|y.sub.n',m|.gtoreq.T.sub.2,
T.sub.2 being a predetermined threshold value, retaining the pixel
set y.sub.n',m, otherwise deleting it; marking retained pixel sets
as y.sub.k, k=1, 2, . . . , K, K being a number of the retained
pixel sets; (S3.7) when an apparent reflectance of a pixel in the
pixel set y.sub.k in the blue/coast band .rho..sub.y.sub.k,i<T,
i=1, 2, . . . , |y.sub.k|, |y.sub.k| being the number of pixels in
the set y.sub.k, T being an OTSU threshold of the apparent
reflectance of the pixel set y.sub.k in the blue/coast band,
allocating the pixel into a target set Y.sub.s, otherwise
allocating the pixel to an error categorization set Y.sub.t; (S4)
for the target set Y.sub.s, acquiring the aerosol optical thickness
value AOT of each pixel through the steps of; (S4.1) marking a p-th
pixel set in the set Y.sub.s as y.sub.s,p, p=1, 2, . . . ,
|Y.sub.s|, |Y.sub.s| being a number of pixel sets in the set
Y.sub.s, for each pixel set y.sub.s,p, acquiring a pixel
y.sub.s,p,min with the minimum apparent reflectance in the
blue/coast band, the pixel y.sub.s,p,min being a clean pixel;
(S4.2) setting an aerosol optical thickness value AOT of the clean
pixel as .epsilon., .epsilon. being a preset minimum value,
retrieving a ground surface reflectivity of the clean pixel
y.sub.s,p,min from the aerosol optical thickness look-up table
generated in step S2; setting ground surface reflectivities of all
pixels in the pixel set y.sub.s,p as being equal to the ground
surface reflectivity of the clean pixel y.sub.s,p,min; (S4.3)
retrieving aerosol optical thickness values AOTs of all pixels in
the target set Y.sub.s; (S5) performing retrieval on the error
categorization set Y.sub.t through the steps of: (S5.1) traversing
all pixels in the set Y.sub.s, to search for pixels with no values
within a predetermined radius, designating an aerosol thickness
value of a pixel with no value as the aerosol thickness value of
the pixel, and marking a set comprising all designated pixels as
P.sub.s; (S5.2) for each pixel in the set P.sub.s, obtaining
parameters corresponding to the designated aerosol optical
thickness value from the aerosol optical thickness look-up table,
and then calculating and obtaining a ground surface reflectivity of
the pixel according to the apparent reflectance; (S5.3) gridding
the multi-band remote sensing image according to a predetermined
side length; (S5.4) marking a q-th pixel set in the set Y.sub.t as
y.sub.t,q, q=1, 2, . . . , |Y.sub.t|, |Y.sub.t| being a number of
the pixel sets in the set Y.sub.t; for each pixel set y.sub.t,q,
finding an intersection with s from each grid partitioned in step
S5.3, and setting the ground surface reflectivity of all pixels of
the pixel set y.sub.t,q in a current grid as being equal to an
average ground surface reflectivity value of all pixels in the
intersection; (S5.5) performing aerosol thickness value retrieval
on each pixel assigned with the ground surface reflectivity in the
step S5.4, to obtain the aerosol thickness value of the pixel.
2. The method for retrieving atmospheric aerosol based on
statistical segmentation according to claim 1, wherein the interval
.lamda..sub.1 in step S3.1 is within a range of
0.002.ltoreq..lamda..sub.1.ltoreq.0.01.
3. The method for retrieving atmospheric aerosol based on
statistical segmentation according to claim 1, wherein the interval
.lamda..sub.2 in step S3.4 is within a range of
0.002.ltoreq..lamda..sub.2.ltoreq.0.01.
4. The method for retrieving atmospheric aerosol based on
statistical segmentation according to claim 1, wherein the minimum
value .epsilon. in step S4.2 is 0.01.
5. The method for retrieving atmospheric aerosol based on
statistical segmentation according to claim 1, wherein the
retrieving method of the ground surface reflectivity in step S4.2
and step S5.2 is: searching for respective parameters corresponding
to the aerosol optical thickness value AOT of a pixel from the
aerosol optical thickness look-up table, and calculating the ground
surface reflectivity based on the apparent reflectance of the
pixel.
6. The method for retrieving atmospheric aerosol based on
statistical segmentation according to claim 1, wherein in step S4.3
and step S5.5 the aerosol thickness value of each pixel is
retrieved through the steps of: obtaining parameters corresponding
to each aerosol optical thickness value AOT from the look-up table,
and calculating an apparent reflectance .rho..sub.i corresponding
to each aerosol thickness value AOT, where i=1, 2, . . . , L being
the number of the aerosol thickness value in the look-up table; for
each pixel, searching for an apparent reflectance closest to its
own apparent reflectance from L apparent reflectances .rho..sub.i,
and setting a corresponding aerosol optical thickness value AOT as
being the aerosol thickness value of the pixel.
Description
[0001] This application claims priority of the Chinese Patent
Application No. 201910454155.5, filed on May 29, 2019, which is
incorporated herein by reference in its entirety.
TECHNICAL FIELD
[0002] The present invention relates to the field of information
remote sensing technologies, and more particularly to a method for
retrieving atmospheric aerosol based on statistical
segmentation.
BACKGROUND
[0003] The atmospheric aerosol is a multi-phase system formed by
atmosphere as well as solid and liquid particles suspending
therein. It is a mixture consisting of molecular groups and liquid
or solid particles suspending in the air and having certain
stability, a small sedimentation rate, and a size ranging from one
micrometer to dozens of micrometers. Aerosol particles mainly
originate from artificial sources such as industrial activities,
biological burning and the like, as well as natural sources such as
dusts, offshore marine particles and the like.
[0004] Research shows that the atmospheric aerosol not only affects
radiation balance of an earth surface layer system, but also causes
the earth-atmosphere system to cool by adjusting a reflectivity of
an earth atmosphere system through scattering solar shortwave
radiation. It also causes a heating process through absorbing the
solar radiation, and changes microphysical properties of clouds by
affecting the microphysical process of formation of the clouds.
Aerosol also significantly influences atmospheric chemical
processes and biogeochemical cycle. At the same time, the aerosol
particles contain some particles harmful to human body, which may
cause serious problem to the health of human beings, especially in
heavily populated urban areas with many industries. Therefore,
retrieval of the atmospheric aerosol has a significant meaning to
research of global climate change as well as monitoring and
management of atmospheric pollution.
[0005] Currently, researchers home and abroad have made quite some
achievements in terms of aerosol remote sensing retrieval. They
also pay much attention to monitoring of the aerosol in designing
novel sensors. However, most of the conventional retrieval
algorithms are directed to large-size aerosol having average
properties, and quite limited in terms of applications. For
example, such algorithms are only adapted to dark ground surface.
At present, there is no effective aerosol retrieval algorithm
adapted to bright ground surface areas such as cities.
SUMMARY
[0006] An object of the present invention is to provide a method
for retrieving atmospheric aerosol based on statistical
segmentation. Two parts are partitioned by way of statistical
segmentation, and aerosol thickness values thereof are retrieved
using different methods, thereby improving accuracy and resolution
of the retrieval result of the bright ground surface area.
[0007] In order to meet the above-mentioned objective of the
present invention, the method for retrieving atmospheric aerosol
based on statistical segmentation in the present invention
comprises steps of:
[0008] S1: acquiring a multi-band high-resolution remote sensing
image of an analyzed area using satellites, and performing
radiometric calibration to obtain a multi-band remote sensing image
comprising an apparent reflectance, wherein the multi-band remote
sensing image comprises a mid-infrared 1.6 micrometer band and a
mid-infrared 2.1 micrometer band, and selecting a blue/costal band
from the multi-band remote sensing image as an retrieval band;
[0009] S2: inputting the multi-band remote sensing image into an
atmospheric radiative transfer model, obtaining a corresponding
aerosol optical thickness look-up table according to the retrieval
band, wherein the look-up table comprises parameters corresponding
to each aerosol optical thickness value AOT for calculating the
apparent reflectance;
[0010] S3: for all pixels in the multi-band remote sensing image,
performing categorization using a statistical segmentation method,
wherein the segmentation method comprises steps of:
[0011] S3.1: partitioning an apparent reflectance range of the
mid-infrared 2.1 micrometer band into N segments .phi..sub.n, n=1,
2, . . . , N, according to a predetermined interval
.lamda..sub.1;
[0012] S3.2: based on apparent reflectances of respective pixels in
the multi-band remote sensing image in the mid-infrared 2.1
micrometer band, allocating the respective pixels into
corresponding segments, to obtain pixel sets x.sub.n corresponding
to respective segments g;
[0013] S3.3: for each pixel set x.sub.n obtained in step S3.2, in
condition that a number of pixels in the pixel set
x.sub.n|x.sub.n.gtoreq.T.sub.1, T.sub.1 being a predetermined
threshold value, retaining the pixel set, otherwise deleting the
pixel set; marking retained pixel sets as x.sub.n', n'=1, 2, . . .
, N', N' being a number of retained pixel sets;
[0014] S3.4: partitioning an apparent reflectance range of the
mid-infrared 1.6 micrometer band into M segments .gamma..sub.m,
m=1, 2, . . . , M according to a predetermined interval
.lamda..sub.2;
[0015] S3.5: based on apparent reflectances of pixels in each pixel
set x.sub.n' in the mid-infrared 1.6 micrometer band, allocating
respective pixels to a corresponding segment, to obtain pixel sets
.gamma..sub.n',m corresponding to respective segments .gamma..sub.m
in the pixel sets;
[0016] S3.6: in condition that a number of pixels in the pixel set
y.sub.n',m|y.sub.n',m|.gtoreq.T.sub.2, T.sub.2 being a
predetermined threshold value, retaining the pixel set y.sub.n',m,
otherwise deleting it; marking retained pixel sets as y.sub.k, k=1,
2, . . . , K, K being a number of the retained pixel sets;
[0017] S3.7: when an apparent reflectance of a pixel in the pixel
set y.sub.k in the blue/coast band .rho..sub.y.sub.k,i<T, i=1,
2, . . . , |y.sub.k|, |y.sub.k| being the number of pixels in the
set y.sub.k, T being an OTSU threshold of the apparent reflectance
of the pixel set y.sub.k in the blue/coast band, allocating the
pixel into a target set Y.sub.s, otherwise allocating the pixel to
an error categorization set Y.sub.t;
[0018] S4: for the target set Y.sub.s, acquiring the aerosol
optical thickness value AOT of each pixel through the steps of:
[0019] S4.1: marking a p-th pixel set in the set Y.sub.s as
y.sub.s,p, p=1, 2, . . . , |Y.sub.s|, |Y.sub.s| being a number of
pixel sets in the set Y.sub.s, for each pixel set y.sub.s,p,
acquiring a pixel y.sub.s,p,min with the minimum apparent
reflectance in the blue/coast band, the pixel y.sub.s,p,min being a
clean pixel;
[0020] S4.2: setting an aerosol optical thickness value AOT of the
clean pixel as .epsilon., .epsilon. being a preset minimum value,
retrieving a ground surface reflectivity of the clean pixel
Y.sub.s,p,min from the aerosol optical thickness look-up table
generated in S2; setting ground surface reflectivities of all
pixels in the pixel set y.sub.s,p as being equal to the ground
surface reflectivity of the clean pixel y.sub.s,p,min;
[0021] S4.3: retrieving aerosol optical thickness values AOTs of
all pixels in the target set Y.sub.s;
[0022] S5: performing retrieval on the error categorization set
Y.sub.t through the steps of:
[0023] S5.1: traversing all pixels in the set Y.sub.s, to search
for pixels with no values within a predetermined radius,
designating an aerosol thickness value of a pixel with no value as
the aerosol thickness value of the pixel, and marking a set
comprising all designated pixels as P.sub.s;
[0024] S5.2: for each pixel in the set P.sub.s, obtaining
parameters corresponding to the designated aerosol optical
thickness value from the aerosol optical thickness look-up table,
and then calculating and obtaining a ground surface reflectivity of
the pixel according to the apparent reflectance;
[0025] S5.3: gridding the multi-band remote sensing image according
to a predetermined side length;
[0026] S5.4: marking a q-th pixel set in the set Y.sub.t as
y.sub.t,q=1, 2, . . . , |Y.sub.t|, |Y.sub.t| being a number of the
pixel sets in the set Y.sub.t; for each pixel set y.sub.t,q,
finding an intersection with P.sub.s from each grid partitioned in
step S5.3, and setting the ground surface reflectivity of all
pixels of the pixel set y.sub.t,q in a current grid as being equal
to an average ground surface reflectivity value of all pixels in
the intersection;
[0027] S5.5: performing aerosol thickness value retrieval on each
pixel assigned with the ground surface reflectivity in the step
S5.4, to obtain the aerosol thickness value of the pixel.
[0028] In the method for retrieving atmospheric aerosol based on
statistical segmentation in the present invention, the aerosol
optical thickness look-up table of the retrieval band are obtained
first, then the pixels are partitioned and screened according to
the apparent reflectance segments of the mid-infrared 2.1
micrometer band. After that the retained pixel sets are further
partitioned and screened according to the apparent reflectance
segments of the mid-infrared 1.6 micrometer band. Finally the
obtained pixel sets are partitioned into two categories according
to the respective OTSU threshold for apparent reflectance in the
coast band, with pixels smaller than the threshold in one category,
and the remaining pixels in another category. The category with
pixels smaller than the threshold is taken as the target set for
retrieval. Specifically, each of the pixel sets are first searched
for the clean pixel, then the ground surface reflectivity of the
clean pixel is taken as the ground surface reflectivity of the
whole pixel set, thereby obtaining the aerosol optical thickness
value through retrieval. After that these pixels are taken as
references to perform retrieval on the other category.
[0029] The present invention adopts a novel method when calculating
the ground surface reflectivity. According to this method, the
ground surface reflectivity is determined according to the clean
pixel, instead of depending on a dark pixel. Therefore, as long as
completion of the statistical segmentation is satisfied, the
retrieval can be performed. Consequently, the present invention
also has good retrieval effect for bright ground surface area, and
has wider applicability compared with the conventional dark pixel
algorithm.
BRIEF INTRODUCTION OF THE ACCOMPANYING DRAWINGS
[0030] FIG. 1 schematically illustrates a flow chart of a method
for retrieving atmospheric aerosol based on statistical
segmentation in accordance with an embodiment of the present
invention;
[0031] FIG. 2 is a diagram of an image used in the present
example;
[0032] FIG. 3 schematically illustrates a flow chart of statistical
segmentation in accordance with an embodiment of the present
invention;
[0033] FIG. 4 schematically illustrates partitioning pixels of Band
7 in accordance with an embodiment of the present invention;
[0034] FIG. 5 schematically illustrates spectral curves of ground
surface reflectivity;
[0035] FIG. 6 is a partial histogram of an apparent reflectance of
a pixel set y.sub.k in Band 1.
[0036] FIG. 7 schematically illustrates a flow chart of performing
retrieval on each pixel set y.sub.s,p in the set Y.sub.s;
[0037] FIG. 8 schematically illustrates a flow chart of performing
retrieval on the set Y.sub.t;
[0038] FIG. 9 schematically illustrates a gridding result of the
image shown in FIG. 2; and
[0039] FIG. 10 schematically illustrates a result of retrieving a
multi-band remote sensing image shown in FIG. 2 according to the
present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0040] In the following, specific embodiments of the present
invention will be described in detail with reference to the
accompanying drawings such that a person skilled in the art can
better understand the invention. In the following description, it
is noted that well-known functions and configurations are not
described in detail to avoid obscuring the present invention.
Embodiments
[0041] FIG. 1 is a flow chart of a method for retrieving
atmospheric aerosol based on statistical segmentation according to
an embodiment of the present invention. As shown in FIG. 1, the
method for atmospheric aerosol retrieval based on statistical
segmentation comprises the following steps:
[0042] S101: acquiring a multi-band remote sensing image.
[0043] A multi-band high-resolution remote sensing image of an
analyzed area is acquired through satellites, and radiometric
calibration is performed to obtain a multi-band remote sensing
image comprising an apparent reflectance. The multi-band remote
sensing image comprises a mid-infrared 1.6 micrometer band and a
mid-infrared 2.1 micrometer band, and a blue/coast band is selected
from the multi-band remote sensing image as a retrieval band.
[0044] Currently the LANDSAT satellite is used to acquire the
multi-band high-resolution remote sensing image. In a LANDSAT 8 OLI
image there are 11 bands, namely, Band1 costal band, Band2 blue
band, Band3 green band, Band4 red band, Band5 near-infrared band,
Band6 mid-infrared 1.6 micrometer band, Band1 mid-infrared 2.1
micrometer band, Band8 full-color band, Band9 cirrus band, Band10
thermal infrared band 1, and Band11 thermal infrared band 2. Band6
mid-infrared band and Band1 mid-infrared band are used in the
present invention. The retrieval band selected in the present
example is Band1, this is because Band1 in the LANDSAT 8 OLI image
is more sensitive to the aerosol, and can therefore improve the
retrieval accuracy.
[0045] An image taken on the 42.sup.nd day of the year 2015 in
Beijing is used in the present example. FIG. 2 is the image used in
the present example. As shown in FIG. 2, this image includes bright
ground surface areas (with relatively light-color).
[0046] S102: acquiring an aerosol optical thickness (AOT) look-up
table.
[0047] The multi-band remote sensing image obtained in step S101 is
input into an atmospheric radiative transfer model, thereby
arriving at a corresponding aerosol optical thickness look-up table
according to the retrieval band. The look-up table includes
parameters corresponding to each aerosol thickness value AOT for
calculating the apparent reflectance. In the present example,
assuming that an underlying surface is a Lambertian surface, its
apparent reflectance .rho..sub.TOA is calculated as:
.rho. T .times. O .times. A = .rho. 0 + T s T v .rho. s 1 - .rho. s
S . ##EQU00001##
[0048] where .rho..sub.0 is an atmospheric path radiation
reflectivity, .rho..sub.s a ground surface reflectivity, S is a
downward hemispherical emissivity of a lower atmospheric layer,
T.sub.s is a total transmittance of incident light from top of the
atmosphere to the ground surface, T.sub.v is a total transmittance
of light upwardly entering a satellite sensor field. Therefore it
can be seen that in the present example the parameters which need
to be included in the look-up table include the atmospheric path
radiation reflectivity, the atmosphere hemispherical emissivity,
and a product of the total transmittance of the incident light from
the top of the atmosphere to the ground surface and the total
transmittance upwardly entering a satellite sensor field.
[0049] Currently, there are many atmospheric radiative transfer
models. In the embodiment, the most common 6S radiative transfer
model is used. In addition to the multi-band remote sensing image,
other parameters that need to be input into the 6S radiative
transfer model include: date, zenith angle, and azimuth angle
corresponding to the multi-band remote sensing image, an aerosol
type and atmosphere mode, a step size of the aerosol optical
thickness value and so on. The atmosphere mode selected in the
present example is mid-latitude winter, the aerosol type is a
continental type, and the selected retrieval band is Band1 costal
band. Table 1 is a look-up table acquired in the present
example.
TABLE-US-00001 TABLE 1 AOT .rho..sub.0 S T.sub.s T.sub.v 0.01
0.07883 0.78547 0.13188 0.02 0.08103 0.77265 0.13571 . . . . . . .
. . . . . 2.99 0.24536 0.06927 0.29881
[0050] S103: performing statistical segmentation on the pixels.
[0051] For all pixels in the multi-band remote sensing image,
segmentation is performed using a statistical segmentation method.
As the mid-infrared 1.6 micrometer band and mid-infrared 2.1
micrometer band have relatively long wavelengths, and they are not
susceptible to aerosol interference. Therefore, the apparent
reflectances of the mid-infrared 1.6 micrometer band and the
mid-infrared 2.1 micrometer band are used to segment the pixels in
the present invention, and aerosol optical thickness value
retrieval is performed with the costal band. Since the LANDSAT 8
OLI image is used in the present example, the mid-infrared 1.6
micrometer band is Band6, the mid-infrared 2.1 micrometer band is
Band7, and the costal band is Band1.
[0052] FIG. 3 is a flow chart of categorizing pixels in accordance
with an embodiment of the present invention. As shown in FIG. 3,
categorizing pixels in the present invention comprises the
following steps.
[0053] S301: partitioning the apparent reflectance of Band 7 into
segments.
[0054] The apparent reflectance range of Band7 is partitioned into
N segments .phi..sub.n, n=1, 2, . . . , N, according to a
predetermined interval .lamda..sub.1. The partitioning interval
.lamda..sub.1 can be determined as needed. Generally, the interval
.lamda..sub.1 is within a range of
0.002.ltoreq..lamda..sub.1.ltoreq.0.01.
[0055] S302: allocating the pixels.
[0056] Based on apparent reflectances in Band7 of respective pixels
of the multi-band remote sensing image, the pixels are allocated to
corresponding segments, to obtain pixel sets x.sub.n corresponding
to respective segments .phi..sub.n. Assuming that an apparent
reflectance of a pixel at Band7 is marked as p, then a serial
number of its corresponding segment will be .left
brkt-top..rho./.lamda..right brkt-bot., .left brkt-top. .right
brkt-bot. is rounding up to an integer.
[0057] FIG. 4 is a diagram illustrating partitioning part of the
pixels of Band7 at an interval of 0.005 in the present example.
FIG. 4(a) is an overall diagram of the partitioning, and FIG. 4(b)
is a partial diagram of the partitioning. As shown in FIG. 4, by
partitioning the apparent reflectance range of Band7 into segments,
the pixels in the multi-band remote sensing image can be
partitioned into segments.
[0058] S303: screening pixel sets.
[0059] N pixel sets x.sub.n obtained in step S302 are evaluated one
by one. If a number of pixels in the pixel set
x.sub.n|x.sub.n|.gtoreq.T.sub.1, T.sub.1 being a predetermined
threshold value, this pixel set is retained, otherwise it is
deleted. The retained pixel sets are marked as x.sub.n', n'=1, 2, .
. . , N', N' is the number of the pixel sets retained after the
screening. The value of the threshold T.sub.1 may also be set as
needed. Apparently, the higher the resolution of the multi-band
remote sensing image and the larger the interval .lamda..sub.1 is,
and the more pixels are included in each segment. Correspondingly,
the threshold value T.sub.1 may also be set to be slightly higher,
otherwise the threshold value T.sub.1 may be set to be slightly
lower. In the present example, T.sub.1=100000.
[0060] S304: partitioning the apparent reflectance of Band6 into
segments.
[0061] The apparent reflectance range of Band6 is partitioned into
M segments .gamma..sub.m, m=1, 2, . . . , M, according to a
predetermined interval .lamda..sub.2. Likewise, the partitioning
interval .lamda..sub.2 can be determined as needed, and may be
within the range of 0.002.ltoreq..lamda..sub.2.ltoreq.0.01.
[0062] S305: further partitioning the pixel sets.
[0063] Each pixel set x.sub.n' retained in step S303 is further
partitioned. The further partitioning may be performed in the
following way: each pixel within each pixel set x.sub.n' is
allocated into a corresponding segment .gamma..sub.m according to
an apparent reflectance of the pixel at Band6, to obtain a pixel
set y.sub.n',m corresponding to respective segment .gamma..sub.m in
this pixel set. Apparently, after further partitioning the pixel
sets, N'.times.M pixel sets in total are obtained.
[0064] S306: re-screening the pixel sets.
[0065] The N'.times.M pixel sets obtained in step S305 are screened
again. If a number of pixels in the pixel set
y.sub.n',m|y.sub.n',m|.gtoreq.T.sub.2, T.sub.2 being a
predetermined threshold value, this pixel set is retained,
otherwise it is deleted. The pixel set retained after the screening
is marked as y.sub.k, k=1, 2, . . . , K, K is the number of the
pixel sets retained after the screening. Like the threshold value
T.sub.1, the threshold value T.sub.2 may also be set as needed.
Since the pixel set y.sub.n',m is a pixel set obtained after
further partitioning, the number of pixels in the pixel set
y.sub.n',m will definitely be less, therefore T.sub.2 is definitely
smaller than T.sub.1. In the present example, T.sub.2=5000.
[0066] S307: categorizing the pixel sets in Band1.
[0067] The K pixel sets y.sub.k obtained after screening in step
S306 are categorized according to an OTSU threshold T and an
apparent reflectance of each set y.sub.k in Band1. If a pixel in
the pixel set y.sub.k has an apparent reflectance
.rho..sub.y.sub.k,i<T, i=1, 2, . . . , |y.sub.k|, where
|y.sub.k| denotes a number of pixels in the pixel set y.sub.k, the
pixel y.sub.k,i, is categorized into a target set Y.sub.s,
otherwise it is categorized into an error categorization set
Y.sub.t.
[0068] The pixels in the multi-band remote sensing image are
segmented according to the apparent reflectances of Band6 and Band1
through the above steps, and get the final categorization result in
Band1. FIG. 5 schematically illustrates spectral curves of ground
surface reflectivities. FIG. 5 shows spectral curves of ground
surface reflectivities of some objects in an ENVI standard spectrum
database. It can be seen by studying spectral curves of a lot of
objects that the same or different objects may have different
ground surface reflectivity curves under different conditions, and
the reflectivity curves may overlap with each other at the
mid-infrared 1.6 micrometer band and the mid-infrared 2.1
micrometer band. However, it rarely happens like this, especially
for regular objects, and the categorization error can be further
reduced by the subsequent OTSU threshold method. FIG. 6 is a
partial histogram of the apparent reflectance of the pixel set in
Band 1. FIG. 6 shows that after twice segmentation, the histogram
of the apparent reflectance of the pixels in the pixel set in Band1
has a multivariate normal distribution, and there is a clear
highest peak. The study has found that the pixels corresponding to
the highest peak are correctly categorized pixels, and the
remaining short peaks are erroneously categorized pixels.
Therefore, these two types of pixels are separated by the OTSU
threshold method. Therefore, although there may be errors in the
categorization method used in the present invention, the pixels in
the multi-band remote sensing image can still be partitioned into
many segments using the mid-infrared 1.6 micrometer band and the
mid-infrared 2.1 micrometer band, and the result is further
improved by the OTSU threshold method. If no error exists in these
categorization results, eventually, the pixels belonging to the
same segment should have approximately the same ground surface
reflectivity curve, and their ground surface reflectivities at the
retrieval band should be approximately equal.
[0069] S104: performing retrieval on the sets Y.sub.s.
[0070] In the present invention, the sets Y.sub.s, taken as a
reference set, are processed using a method of retrieving polluted
pixels based on clean pixels.
[0071] In the retrieval of aerosol, it is very important to obtain
the ground surface reflectivity. It is shown by experiments that
each pixel segment in the target sets Y.sub.s has few errors, this
is mainly because each pixel set in the target sets Y.sub.s is the
same type of regular object, for example, vegetation and bare earth
and so on. In the meantime, some errors can be further eliminated
by the OTSU threshold method. Moreover, the probability that each
pixel set in Y.sub.s containing clean pixels is relatively high.
The p-th pixel set in Y.sub.s is denoted as y.sub.s,p, p=1, 2, . .
. , |Y.sub.s|, |Y.sub.s| being a number of the pixel sets in the
sets Y.sub.s. Thus, as long as the ground surface reflectivities of
the clean pixels in each pixel set are obtained, the ground surface
reflectivities of all pixels in the pixel set can be obtained.
Therefore, it is necessary to obtain the ground surface
reflectivities of the clean pixels in each pixel set first.
[0072] FIG. 7 is a diagram illustrating a flow chart of retrieving
each pixel set y.sub.s,p in the sets Y.sub.s. As shown in FIG. 7, a
detailed method for retrieving the pixel set y.sub.s,p is as
follows.
[0073] S701: searching for a clean pixel.
[0074] For each pixel set y.sub.s,p in the set Y.sub.s, a pixel
with the minimum apparent reflectance in Band1 is searched out. The
minimum value cannot be less than or equal to 0, and it is recorded
as a clean pixel y.sub.s,p,min.
[0075] S702: calculating the ground surface reflectivity.
[0076] Assuming that the aerosol thickness value AOT of the clean
pixel Y.sub.s,p,min is .epsilon., where .epsilon. is a preset
minimum value, various parameters corresponding to the aerosol
optical thickness value .epsilon. are searched out from the aerosol
optical thickness look-up table generated in S102. The ground
surface reflectivity is calculated based on the apparent
reflectance of the clean pixel y.sub.s,p,min. In the present
embodiment, .epsilon.=0.01.
[0077] S703: performing retrieval on the set y.sub.s,p.
[0078] It can be seen from the previous analysis that the ground
surface reflectivity corresponding to the clean pixie y.sub.s,p,min
is actually the ground surface reflectivity of the whole pixel set
y.sub.s,p. Therefore, it can be used to retrieve the aerosol
optical thickness value of each pixel in the pixel set y.sub.s,p.
The retrieval method may be selected as needed. A retrieval method
used in the present example is as follows:
[0079] obtaining parameters corresponding to each aerosol thickness
value AOT from the look-up table, and calculating an apparent
reflectance .rho..sub.i corresponding to each aerosol thickness
value AOT, where i=1, 2, . . . , L, L is the number of the aerosol
thickness value in the look-up table.
[0080] For each pixel, an apparent reflectance closest to its
apparent reflectance is searched from the L apparent reflectances
.rho..sub.i, and an aerosol thickness value AOT corresponding
thereto is just the aerosol thickness value of this pixel.
[0081] S105: performing retrieval on the set Y.sub.t.
[0082] Since retrieval is performed on the pixels in the set
Y.sub.s in step S104 to obtain the aerosol optical thickness value
of each pixel, retrieval for the pixels in the set Y.sub.t is
performed by taking the pixels in the set Y.sub.s as references.
FIG. 8 is a flow chart of performing retrieval on the set Y.sub.t.
As shown in FIG. 8, detailed steps of performing retrieval on the
set Y.sub.t are as follows.
[0083] S801: expanding reference pixels.
[0084] Each pixel in the set Y.sub.s is traversed, to search for
pixels with no value within a predetermined radius. The aerosol
thickness value of this pixel is designated as aerosol thickness
values of the pixels with no value. A pixel set of all filled and
assigned pixels is marked as P.sub.s. The searching radius r is
generally set as needed, and the smaller the radius is, the more
precise the filled aerosol thickness value will be, though the
number of pixels remained with no value will be increased.
Generally, the searching radius r is within the range of
1.ltoreq.r.ltoreq.10.
[0085] S802: calculating the ground surface reflectivities of the
pixels in the set P.sub.s.
[0086] For each pixel in the set P.sub.s, parameters corresponding
to the filled aerosol thickness value are obtained from the aerosol
optical thickness look-up table, and then the ground surface
reflectivity of this pixel is calculated and obtained according to
the apparent reflectance.
[0087] S803: gridding the multi-band remote sensing image.
[0088] Considering that the objects have certain correlation in
geographic space, the multi-band remote sensing image is
partitioned into grids according to a predetermined side length.
The predetermined side length can be set according to a resolution
of the multi-band remote sensing image. FIG. 9 is a gridding result
of the image shown in FIG. 2.
[0089] S804: acquiring the ground surface reflectivity.
[0090] A q-th pixel set in the set Y.sub.t is marked as y.sub.t,q,
q=1, 2, . . . , |Y.sub.t|, |Y.sub.t| representing the number of the
pixel sets in the set Y.sub.t. For each pixel set y.sub.t,q, an
intersection with P.sub.s is obtained for each grid partitioned in
step S803. Then the ground surface reflectivity of all pixels of
the pixel set y.sub.t,q in the current grid is set to be equal to
an average ground surface reflectivity value of all pixels in this
intersection. Thus, the ground surface reflectivities of the pixels
in the set Y.sub.t are obtained.
[0091] S805: retrieving the aerosol thickness value.
[0092] Aerosol thickness value retrieval is performed for each
pixel assigned with the ground surface reflectivity in step S804,
to obtain the aerosol thickness value of this pixel.
[0093] Since the pixel sets are screened in step S103 in the
present invention, the sets Y.sub.s and Y.sub.t do not contain all
pixels of the multi-band remote sensing image, neither can
retrieval be performed on all of the pixels in step S105. A number
of the remaining pixels with no value can be reduced by adjusting
the threshold of each segment or adjusting the grid size, so the
remaining pixels with no value have little effect on the overall
retrieval result. FIG. 10 schematically illustrates a retrieval
result for the multi-band remote sensing image shown in FIG. 2
using the present invention, in which the blank portion represents
pixels with no value or water pixels. It can be seen from the
result shown in FIG. 10 that the present invention also has good
retrieval effect for bright ground surface areas, and therefore has
a wider applicability compared with the conventional dark pixel
algorithm.
[0094] In order to prove the accuracy of the present invention,
data of AERONET (Aerosol Robotic Network) ground observation
network is downloaded from the website
http://aeronet.gsfc.nasa.gov/. The retrieval results of the present
invention are compared with the ground observation data and the
retrieval results of the dark pixel algorithm, and two selected
sites are both located in the city, i.e. belonging to bright ground
surface areas. Table 2 shows comparison of the retrieval results of
the present invention, the dark pixel algorithm retrieval results
and the ground surface observation data of the graph as shown in
FIG. 2.
TABLE-US-00002 TABLE 2 Site Beijing Beijing-RADI Observation data
0.1393 0.1575 Retrieval result of 0.66 0.64 dark pixel method
Retrieval result of 0.21 0.47 the present invention Error of the
dark 0.5207 0.4825 pixel method Error of the present 0.0707 0.3125
invention
[0095] As shown in Table 2, the retrieval results of the present
invention are closer to the observation data of the actual sites,
and their errors are smaller. It can be seen that for the bright
ground surface area, the present invention has good retrieval
results. Therefore compared with the dark pixel method, the present
invention has a wider application scope.
[0096] The above is only the preferred embodiments of the invention
and is not intended to limit the invention. For a person skilled in
the art, the invention may have a variety of changes and
modifications. Any change, equivalent replacement, improvement made
within the spirit and principle of the present invention should be
included in the protection scope of the invention.
* * * * *
References