US 12,633,117 B2 · Method for extracting urban flood inundation extent based on remote sensing images

Abstract

A method for extracting urban flood inundation extent based on remote sensing images includes: acquiring and preprocessing a Fengyun-4 satellite remote sensing image to obtain a preprocessed remote sensing image, and dividing the preprocessed remote sensing image into a thin cloud area and a thick cloud area; removing thin clouds and thick clouds in the preprocessed remote sensing image; extracting a first water body area from the cloud-removed remote sensing image to obtain a first urban inundation extent; acquiring a radar image at a same region and a same time as the remote sensing image, and extracting a second water body area from the radar image to obtain a second urban inundation extent; and training a super-resolution convolutional neural network according to the first urban inundation extent and the second urban inundation extent, and using a trained super-resolution convolutional neural network to obtain a reconstructed higher-resolution urban flood inundation extent.

A method for extracting urban flood inundation extent based on remote sensing images, comprising:

acquiring and preprocessing a remote sensing image from a Fengyun-4 satellite to obtain a preprocessed remote sensing image, and dividing the preprocessed remote sensing image by using a convolutional neural network into a thin cloud area and a thick cloud area;
removing thin clouds in the thin cloud area by using homomorphic filtering, and removing thick clouds in the thick cloud area by using a replacement method, to thereby obtain a cloud-removed remote sensing image;
extracting a first water body area from the cloud-removed remote sensing image to obtain a first urban inundation extent;
acquiring a radar image at a same region and a same time, and extracting a second water body area from the radar image by using a maximum between-class variance method to obtain a second urban inundation extent;
and
training a super-resolution convolutional neural network according to the first urban inundation extent and the second urban inundation extent to obtain a trained super-resolution convolutional neural network, and using the trained super-resolution convolutional neural network to obtain a reconstructed higher-resolution urban flood inundation extent;
wherein the extracting a first water body area from the cloud-removed remote sensing image to obtain a first urban inundation extent comprises:
calculating, based on cloud-removed remote sensing image data, a modified normalized difference water index (MNDWI) according to a green-band reflectance and a red-band reflectance, through the following formula:
MNDWI
=
ρ
G
⁢
r
⁢
e
⁢
e
⁢
n
-
ρ
MIR
ρ
G
⁢
r
⁢
e
⁢
e
⁢
n
+
ρ
MIR
where ρGreen represents the green-band reflectance, and ρMIR represents a mid-infrared band reflectance;
classifying a part of the cloud-removed remote sensing image with the MNDWI greater than a threshold MNDWI0 as a first target water body, classifying another part of the cloud-removed remote sensing image with the MNDWI less than or equal to the threshold as a first background, and determining the part of the cloud-removed remote sensing image corresponding to the first target water body as the first urban inundation extent;
and
calculating, based on the first target water body, the first water body area using the following formula:
A
w
=
P
w
⁢
A
t
⁢
otal
P
total
,
where Pw represents a number of water pixels in a study region, Ptotal represents a total number of pixels in the study region, Aw represents the first water body area in the study region, and Atotal represents a total image area of the study region;
wherein the extracting a second water body area from the radar image by using a maximum between-class variance method to obtain a second urban inundation extent comprises:
converting the radar image into a grayscale image and performing normalization on the grayscale image to map pixel values of the grayscale image into a range of 0-255;
counting a number of pixels at each gray level in the grayscale image to form a gray-level histogram;
calculating a total number N of pixels in the grayscale histogram through the following formula:
N
=
∑
(
n
i
)
where ni represents a total number of pixels with a gray level i in the grayscale histogram, and i is in a range of 0-255;
calculating a probability of each gray level, through the following formula:
p
i
=
n
i
/
N
dividing the pixels in the grayscale histogram into two classes according to a pixel value threshold t0, where a pixel set with pixel values less than t0 is recorded as a class A, and a pixel set with pixel values greater than or equal to t0 is recorded as a class B;
calculating a weight pA(t) of the class A and a weight pB(t) of the class B, through the following formulas:
p
A
(
t
)
=
∑
0
≤
i
<
t
0
⁢
p
i
,
p
B
(
t
)
=
∑
t
0
≤
i
<
225
⁢
p
i
,
calculating a grayscale mean mA(t) of the class A and a grayscale mean mB(t) of the class B through the following formulas:
m
A
(
t
)
=
∑
0
≤
i
<
t
0
⁢
(
i
×
p
i
)
P
A
(
t
)
,
m
B
(
t
)
=
∑
t
0
≤
i
<
225
⁢
(
i
×
p
i
)
P
B
(
t
)
,
calculating a grayscale mean of the grayscale image through the following formula:
m
G
=
∑
0
≤
i
≤
255
⁢
(
i
×
p
i
)
,
calculating a between-class variance between the class A and the class B through the following formula:
σ
2
=
p
A
(
t
)
×
(
m
A
(
t
)
-
m
G
)
2
+
p
B
(
t
)
×
(
m
B
(
t
)
-
m
G
)
2
,
traversing all target pixel value thresholds to from 0 to 255, calculating the between-class variance σ2 corresponding to each of the target pixel value thresholds, and finding a pixel value threshold t* of the target pixel value thresholds corresponding to a maximum between-class variance σ2 of the between-class variances σ2 corresponding to the target pixel value thresholds;
and
binarizing the grayscale image using the pixel value threshold t* of the target pixel value thresholds corresponding to the maximum between-class variance σ2, comprising:
setting pixels with pixel values less than t* to 0 to represent a second background, and setting pixels with pixel values greater than or equal to t* to 255 to represent a second target water body;
and determining a part of the radar image corresponding to the second target water body as the second urban inundation extent;
and
calculating the second water body area based on the second target water body.

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Method for extracting urban flood inundation extent based on remote sensing images

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Method for extracting urban flood inundation extent based on remote sensing images

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Abstract

Claims6