CN112967308B - Amphibious boundary extraction method and system for dual-polarized SAR image - Google Patents
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Abstract
The invention discloses an amphibious boundary extraction method and system of a dual-polarized SAR image, wherein the method extracts peak-valley values from frequency histograms through counting the frequency histograms of dual-polarized SAR image data, establishes a two-dimensional scattered point coordinate system, takes straight lines as amphibious discriminants of dual-polarized data scattered points, generates single-phase amphibious binary image extraction results, obtains amphibious boundaries with reliable precision, has higher automation degree, and does not need any manual operation in the whole process.
Description
Technical Field
The invention relates to the technical field of computer image processing, in particular to an amphibious boundary extraction method and system for dual-polarized SAR images.
Background
The frequency and probability of extreme weather are increased due to the influence of climate change, and the extreme weather is manifested by frequent flooding and drought disasters. The automatic high-precision extraction of the large-area amphibious boundary is beneficial to real-time or quasi-real-time monitoring/early warning of flood and drought. The main land and water boundary extraction technology mainly comprises the following three steps:
and extracting the land and water boundary by using the optical remote sensing image. This type of image has its inherent disadvantages, and is particularly serious in regions with clouds and rainy south because of the difficulty in penetrating clouds or dense fog due to the radiation from visible light to near infrared spectrum. Because the flood disasters are often accompanied with rainy weather, the optical remote sensing image is difficult to truly play a role in the important application scene.
A single polarization SAR image double-peak method. The method utilizes the characteristic that water and land on the single polarization SAR image show a double-peak structure, and uses the valley value between double peaks as a threshold value of density segmentation, thereby extracting the water-land boundary. The accuracy of this method is questionable, since the so-called bimodal structure is in fact the result of the superposition of two normal distribution structures of the body of water and of the land, the intersection of the two normal distributions being such that, whatever the threshold used, there is always a considerable fraction of misclassification error. The real water body is divided into land by mistake or the real land is divided into water body by mistake at the land-water junction. The amount of information contained in the single-polarization SAR image is limited after all, and the false-division errors can be reduced as much as possible by using the trough values between the two peaks as the threshold values.
A dual polarization SAR image water body index method. By introducing the concept of the water index, the SAR images of the two polarization modes are comprehensively utilized, and the water identification precision is further improved. The water body index is calculated as follows: wi=ln (10×p) 1 *p 2 ) Where p1 and p2 are the backscattering coefficients or the decibelized intensity values, respectively, for the two polarizations. And then, on the basis of the water index, carrying out extraction treatment like a single-pole double-peak method so as to obtain the land and water boundary. The method can obtain more accurate results than single polarization due to the fact that the method utilizes richer information under two polarization modes, but the used method and the single polarization double-peak method are not two-dimensional, and the information of dual polarization is not utilized to the maximum extent.
Disclosure of Invention
Aiming at the problems, the invention provides an amphibious boundary extraction method and system for a dual-polarized SAR image, which aim to fully discover rich information contained in the dual-polarized SAR image so as to accurately define the amphibious boundary in the image.
In order to solve the above technical problems, a first aspect of the present invention provides an amphibious boundary extraction method for dual-polarized SAR image, comprising the following steps:
s1, acquiring dual-polarized SAR image data of a target area;
s2, counting two types of unipolar SAR image data contained in the bipolar SAR image data, respectively obtaining frequency histograms of first type of polarized data and second type of polarized data, and extracting a first water body peak value w from the frequency histograms of the first type of polarized data 1 First land peak value l 1 A first valley v 1 Extracting a second water peak value w from the frequency histogram of the second type of polarization data 2 Second land peak value l 2 A second valley v 2 ；
S3, taking the first type of polarization data as an X axis, taking the second type of polarization data as a Y axis, establishing a Cartesian coordinate system, and marking out W (W 1 ，w 2 )、L(l 1 ，l 2 ) And V (V) 1 ，v 2 ) Three kinds ofFeature points;
s4, passing the W point and the L point to form a straight line k 1 Passing the V point perpendicular to the straight line k 1 Straight line k of (2) 2 Falling on the straight line k 2 The bottom left dual polarized data point is divided into water and falls on the straight line k 2 The upper right dual polarized data points are divided into land and a single phase amphibious map extraction result is generated.
In some embodiments, the S2 is specifically:
s21, dividing the first type of polarization data/the second type of polarization data into blocks by taking preset pixels as units to form a grid formed by M x N image blocks, and merging pixels which are less than the preset pixels into the last image block;
s22, removing background values in the image blocks to obtain effective values, and extracting histogram peak-valley values of the effective values one by one;
s23, randomly extracting 10% of pixels in the effective value to form a set S;
s24, sorting the data of the set S from small to large, taking 1% quantile of the sorted set S as the minimum value of the data range, and taking 99% quantile of the sorted set S as the maximum value of the data range;
s25, equally dividing the data range into 256 intervals, and calculating the number of pixels falling into each interval to obtain a frequency histogram of the first type polarization data/the second type polarization data;
s26, traversing 256 intervals of the frequency histogram, and defining a target interval as the first water peak value w if the pixel value of the current target interval appears for the first time as the maximum value in the 9 nearest intervals 1 The second water peak value w 2 If the pixel value of the current target interval appears for the second time is the maximum value in the nearest 9 intervals, defining the target interval as the first land peak value l 1 The second land peak value l 2 ；
S27, locating the first water peak value w 1 And the first land peak value l 1 The second water peak value w 2 And the second land peak value l 2 The interval in which the smallest value of all the intervals in between is recorded as the first valley value v 1 Said second valley v 2 。
In some embodiments, the method further comprises performing secondary classification if the number difference between the water body pixels and the land pixels contained in the single-phase amphibious binary image extraction result is more than two times.
In some embodiments, the secondary classification includes randomly extracting 10% of pixels with smaller numbers between the water pixels and the land pixels from the water range and the land range of the single-phase amphibious binary image extraction result, taking the union of the pixels extracted from the water range and the land range as a secondary classification calculation object, repeating the steps of S24-S27 and S3-S4, and regenerating the single-phase amphibious binary image extraction result.
In some embodiments, the method further comprises obtaining optical image data closest in time to the dual-polarized SAR image data, extracting an optical amphibian image result from the optical image data by adopting an optical remote sensing image vegetation and water automatic extraction method, defining an intersection of the single-phase amphibian image extraction result and the optical amphibian image result as a combined binary image result, performing grid-to-vector conversion processing on the single-phase amphibian image extraction result, the optical amphibian image result and the combined binary image result to respectively obtain three vector images V 1 、V 2 And V 3 Obtaining a vector diagram V having an intersecting relation with the vector diagram V3 1 Vector diagram V' of all entities in (a) 1 。
In some embodiments, further comprising, for vector diagram V 3 Is configured to perform the following processing: let the current entity be E 3 Computing entity E 3 Is of the area A 3 Calculating to obtain a entity E 3 Vector diagram V' with intersecting relationship 1 Corresponding entity E in (3) 1 Is of the area A 1 Calculating to obtain a entity E 3 Vector diagram V with intersecting relationship 2 Corresponding entity E in (3) 2 Is of the area A 2 Definition A 1 And A 2 A smaller value of A s Definition A 1 And A 2 A larger value of A b Definition of inclusion degree inc=a 3 /A s Definition of the degree of expansion as exp=a b /A s If INC<0.8 or EXP>500%, then entity E 1 From vector diagrams V 1 And deleted.
In some embodiments, the dual polarized SAR image data is preprocessed after the dual polarized SAR image data of the target region is acquired.
Meanwhile, a second aspect of the present invention provides an amphibious boundary extraction system for dual-polarized SAR images, comprising:
the data acquisition device is used for acquiring dual-polarized SAR image data of the target area;
the data analysis device is used for counting two types of unipolar SAR image data contained in the bipolar SAR image data, respectively obtaining frequency histograms of first type of polarized data and second type of polarized data, and extracting a first water peak value w from the frequency histograms of the first type of polarized data 1 First land peak value l 1 A first valley v 1 Extracting a second water peak value w from the frequency histogram of the second type of polarization data 2 Second land peak value l 2 A second valley v 2 ；
The data modeling device is used for establishing a Cartesian coordinate system by taking the first type of polarization data as an X axis and the second type of polarization data as a Y axis, and marking W (W 1 ，w 2 )、L(l 1 ，l 2 ) And V (V) 1 ，v 2 ) Three feature points;
data extraction device for making a straight line k by passing W point and L point 1 Passing the V point perpendicular to the straight line k 1 Straight line k of (2) 2 Falling on the straight line k 2 The bottom left dual polarized data point is divided into water and falls on the straight line k 2 The upper right dual polarized data point is divided into land and single phase amphibious map extraction is generatedAs a result.
The beneficial effects of the invention are as follows: by counting the frequency histogram of the dual-polarized SAR image data, the peak-valley value is extracted from the frequency histogram, a two-dimensional scattered point coordinate system is established, a straight line is used as the amphibious discriminant of the dual-polarized data scattered points, a single-phase amphibious binary image extraction result is generated, the amphibious boundary with reliable precision is obtained, the degree of automation is high, and no manual operation is needed in the whole process.
Drawings
Fig. 1 is a schematic flow chart of an amphibious boundary extraction method for dual-polarized SAR image according to an embodiment of the present invention;
FIG. 2a is a dual polarized SAR image of the area of the east river in Chenzhou, hunan, 12 months 9 days 2020;
fig. 2b is a binary diagram of the amphibious boundary extraction result of the single-phase dual-polarized SAR image;
FIG. 3a is an optical image of a region of an east river in Chenzhou, hunan, 10 days 11, 2020;
FIG. 3b is a binary image of the optical surface boundary result of the optical image;
FIG. 4a is a view of the land and water boundary vector of the Dongjiang lake region at 12 months 9 days 2020 with the shadow removed;
fig. 4b is a land and water binary image of the east river lake region at 12 months 9 days 2020 after shadow removal;
fig. 5 is a schematic structural diagram of an amphibious boundary extraction system for dual-polarized SAR image according to a third embodiment of the present invention.
Detailed Description
In order to make the purposes, technical schemes and advantages of the invention clearer and more specific, the following is a detailed description of a first-class dual-polarized SAR image of a sentinel in the Dongjiang lake area of Chen, hunan, 12 th month and 9 th day in 2020, taken as an example. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the matters related to the present invention are shown in the accompanying drawings.
Example 1
According to fig. 1, the embodiment provides an amphibious boundary extraction method of a dual-polarized SAR image, which comprises the following steps:
s1, acquiring dual-polarized SAR image data of a target area.
In this embodiment, the target area is selected as the Yangtze river lake area in Chen, hunan province, and dual-polarized SAR image data provided by the Sentinel No. (Sentinel-1) is downloaded from an European Space Agency (ESA) official network through a PC or any networking-enabled device, wherein the website of the dual-polarized SAR image data is https:// scihub.
Furthermore, after the dual-polarized SAR image data of the target area is acquired, the dual-polarized SAR image data is preprocessed, and the method mainly comprises the following steps: 1) Track correction, namely automatically downloading an accurate track file and updating Sentinel-1 satellite track state information in an xml metadata file; 2) Thermal noise removal, namely, the accuracy of radar back scattering signals can be affected by the thermal noise of the SAR image, and the signal-to-noise ratio of the SAR image can be improved by performing thermal noise removal; 3) Radiation calibration, converting a backscattering signal received by a sensor into a backscattering coefficient; 4) The method comprises the steps of performing speckle filtering, namely performing filtering processing on coherent speckle noise in an image by using a Refind-Lee filter (an adaptive filter); 5) The terrain correction is carried out, the DEM is automatically downloaded to carry out the terrain correction on the image, and the position deviation caused by the terrain fluctuation is corrected; 6) Decibelation is essentially a logarithmic transformation, and the backscattering coefficient after the decibelation approximates to the common Gaussian distribution, and is favorable for visualization and data analysis.
As shown in fig. 2a, the preprocessed dual polarized SAR image data of 12/9/2020, and the algorithm flow of each image block is identical, the example only intercepts the image block containing 5000 pixels square of the eastern river lake range as an example.
S2, counting two types of unipolar SAR image data contained in the bipolar SAR image data to obtain respectivelyFrequency histograms of the first type of polarization data and the second type of polarization data, and extracting a first water peak value w from the frequency histograms of the first type of polarization data 1 First land peak value l 1 A first valley v 1 Extracting a second water peak value w from the frequency histogram of the second type of polarization data 2 Second land peak value l 2 A second valley v 2 ；
The above S2 specifically includes:
s21, dividing the first type of polarization data/the second type of polarization data into blocks by taking preset pixels as units, forming a grid formed by M.N image blocks, and merging pixels which are less than the preset pixels into the last image block;
in the present embodiment, the first-type polarization data/the second-type polarization data are not segmented, i.e., the original image of no 1*1.
S22, removing background values (generally 0) in the image blocks to obtain effective values, and extracting histogram peak-valley values of the effective values one by one;
s23, 10% of pixels in the effective values are randomly extracted to form a set S, and the set S is used as a primary classification calculation object, so that the calculation speed is higher. As the statistical characteristics of the random samples can be used for representing global statistical characteristics, 10% of sample calculation characteristic values are randomly extracted, and the speed is improved by 10 times. Taking a sentinel satellite one as an example, the extraction of the amphibious boundary in a 300 km by 200 km area only takes less than 10 minutes.
S24, sorting the data of the set S from small to large in order to eliminate the interference of extreme abnormal values, taking 1% quantile of the sorted set S as the minimum value of the data range, and taking 99% quantile of the sorted set S as the maximum value of the data range;
s25, equally dividing the data range into 256 intervals, and calculating the number of pixels falling into each interval to obtain a frequency histogram of the first type of polarization data/the second type of polarization data;
s26, traversing 256 sections, and defining a target if the pixel value of the current target section appears to be the maximum value in the nearest 9 sections (including the section and the left and right sections) for the first timeThe interval is the first water peak value w 1 Second water peak value w 2 If the pixel value of the current target interval appears for the second time is the maximum value in the nearest 9 intervals, defining the target interval as a first land peak value l 1 Second land peak value l 2 ；
S27, locating at the peak value w of the first water body 1 And a first land peak value l 1 Second water peak value w 2 And a second land peak value l 2 The minimum value in all intervals in between is noted as a first valley value v 1 Second valley value v 2 。
S3, establishing a Cartesian coordinate system by taking the first type of polarization data as an X axis and the second type of polarization data as a Y axis, and marking out W (W 1 ，w 2 )、L(l 1 ，l 2 ) And V (V) 1 ，v 2 ) Three characteristic points which respectively represent scattered points with the maximum water body distribution density, the maximum land distribution density and the minimum land distribution density.
S4, passing the W point and the L point to form a straight line k 1 Passing the V point to be perpendicular to the straight line k 1 Straight line k of (2) 2 In straight line k 2 As a discrimination between water and land, falls on the straight line k 2 The bottom left dual polarized data point is divided into water and falls on the straight line k 2 The upper right dual polarized data point is divided into land and a single phase land and water boundary extraction result is generated, which is actually a binary image (the water value is 1, the land value is 0), and is marked as B 1 As shown in fig. 2 b.
According to the invention, through counting the frequency histogram of the dual-polarized SAR image data, the peak-valley value is extracted from the frequency histogram, a two-dimensional scattered point coordinate system is established, a straight line is used as the amphibious discriminant of the dual-polarized data scattered points, a single-phase amphibious binary image extraction result is generated, the amphibious boundary with reliable precision is obtained, the degree of automation is higher, and no manual operation is required in the whole process.
In order to enhance the classification precision, the single-phase amphibious binary image extraction result can be processed by adopting the following steps: and if the number difference between the water body pixels and the land pixels contained in the single-phase amphibious binary image extraction result is more than two times, performing secondary classification. The secondary classification comprises randomly extracting 10% of pixels with smaller values between the water body pixels and the land pixels from the water body range and the land range of the single-phase amphibious binary image extraction result, taking the union of the pixels extracted from the water body range and the land range as a secondary classification calculation object, repeating the steps of S24-S27 and S3-S4, and regenerating the single-phase amphibious binary image extraction result.
In this embodiment, the number of water pixels is N w The number of land pixels is N l Taking the larger value of N b The smaller value of the two is N s If N b /N s >2, the land and water proportions are considered to be quite different, and secondary classification is needed. This is because on the histogram, the dominant party has an annihilation effect on the weak party such that the valley between the peaks moves toward the weak party away from a reasonable position. When the dual-polarized valley values are offset, the straight line for distinguishing also translates, so that the accuracy of the classification result is affected. The secondary classification steps are as follows: randomly extracting N from the primary classified water body range and land range s * And 10% of pixels, taking the union of the two as a secondary classification calculation object, and repeating the steps of S24-S27 and S3-S4, wherein the single-phase amphibious binary image is extracted.
Example two
The three land and water boundary extraction methods in the background technology do not well treat the influence of shadows on the water body. The existence of mountain or tall building and the characteristics of satellite remote sensing oblique photography measurement enable more shadows to exist on the optical or radar images, and how to remove the interference of shadow noise as much as possible on the basis of water body extraction is always a difficult problem for people. Because the imaging modes and imaging mechanisms of the optical satellite and the radar satellite are greatly different, specifically, the optical satellite belongs to passive remote sensing, and the reflectivity of the ground object to solar radiation is recorded. Radar satellites belong to active remote sensing, and the backscattering coefficient of the radar active emission radiation by the ground object is recorded. The imaging position and the observation direction of different satellites at different moments are different, and the relative azimuth angles between the sensor and the ground feature are also different, so that shadows of mountain or building appear on different sides of the entity, which are expressed in two different observations in a short period, and shadows of the same object are completely disjoint or have a less inclusive meaning even if they have an intersecting relationship (smaller entity in two periods is contained in a larger entity). The position of the water body is not changed in a short period, and the expansion degree or the contraction degree can only occur at the boundary of the water and the land, so that the water body has larger inclusion degree. The different points of the water body and the shadow on the time phase characteristic are fully utilized to distinguish the water body and the shadow.
Therefore, the present embodiment adds a method for removing shadows by a multi-source multi-time phase method on the basis of the first embodiment, so as to remove shadows of radar images confused in a water body.
The steps of shadow removal by the multi-temporal method are as follows: firstly, optical image data closest to dual-polarized SAR image data in time is acquired, and no cloud layer shielding is required for a target area.
In this example, the optical image data is the last-stage historical sentinel second-order image data downloaded from the same website, as shown in fig. 3a. The time of the optical image data is marked as 11/10/2020, and the file name is S2B_MSIL2A_2020110T025939_N0214_R032_T49RGJ_2020110T055638. Zip. The method can select manual downloading, and can also use Python to call a function in a sentinelsat library to automatically download data of a designated area and a time range.
Then, an optical remote sensing image vegetation and water body automatic extraction method is adopted to extract an optical amphibious binary image result from the optical image data, and the result is actually a binary image (the water body value is 1, and the land value is 0) and is marked as B 2 As shown in fig. 3 b. The intersection of the single-phase amphibious map extraction result and the optical amphibious map result is defined as the combined binary map result (i.e. B 1 1U B 2 1) to obtain a further binary image, designated B 3 Extracting result B from single-phase amphibious binary image 1 Result B of optical amphibious map 2 Merging binary image results B 3 Performing grid vector conversion processing to obtain three vector images V respectively 1 、V 2 And V 3 Obtaining a vector diagram V having an intersecting relation with the vector diagram V3 1 Vector diagram V of all entity constitution in (3) 1 The shadows having no intersecting relationship at all at different imaging moments are removed so far.
Further shadow removal, for vector diagram V 3 Is configured to perform the following processing: for vector diagram V 3 Is configured to perform the following processing: let the current entity be E 3 Computing entity E 3 Is of the area A 3 Calculating to obtain a entity E 3 Vector diagram V' with intersecting relationship 1 Corresponding entity E in (3) 1 Is of the area A 1 Calculating to obtain a entity E 3 Vector diagram V with intersecting relationship 2 Corresponding entity E in (3) 2 Is of the area A 2 Definition A 1 And A 2 A smaller value of A s Definition A 1 And A 2 A larger value of A b Definition of inclusion degree inc=a 3 /A s Definition of the degree of expansion as exp=a b /A s If INC<0.8 or EXP>500%, then entity E 1 From vector diagrams V 1 Shadows having an intersecting relationship at different times but having a low inclusion or an excessively large expansion are removed so far.
All entities can obtain the current-period amphibious boundary vector diagram with most shadows removed after the treatment, as shown in fig. 4a, which illustrates the amphibious boundary vector diagram of the east river and lake region of year 2020 and 9 after the shadow removal, and fig. 4b is a corresponding amphibious binary diagram, and it can be seen that compared with fig. 2b, the amphibious boundary after the shadow removal is closer to the real environment. In the embodiment, the single-time-phase amphibious binary image extraction result is fused with multi-source multi-time-phase amphibious information, so that interference of radar shadows is removed to the maximum extent, and a amphibious boundary with reliable precision is obtained.
Example III
As shown in fig. 5, an amphibious boundary extraction system for dual polarized SAR image is disclosed, comprising:
the data acquisition device is used for acquiring dual-polarized SAR image data of the target area;
the data analysis device is used for counting two types of unipolar SAR image data contained in the bipolar SAR image data, respectively obtaining frequency histograms of the first type of polarized data and the second type of polarized data, and extracting a first water body peak value w from the frequency histograms of the first type of polarized data 1 First land peak value l 1 A first valley v 1 Extracting a second water peak value w from the frequency histogram of the second type of polarization data 2 Second land peak value l 2 A second valley v 2 ；
The data modeling device is used for establishing a Cartesian coordinate system by taking the first type of polarization data as an X axis and the second type of polarization data as a Y axis, and marking out W (W 1 ，w 2 )、L(l 1 ，l 2 ) And V (V) 1 ，v 2 ) Three feature points;
data extraction device for making a straight line k by passing W point and L point 1 Passing the V point to be perpendicular to the straight line k 1 Straight line k of (2) 2 Fall on straight line k 2 The bottom left dual polarized data point is divided into water and falls on the straight line k 2 The upper right dual polarized data point is divided into land and a single phase amphibious map extraction result is generated and is marked as B 1 。
In this embodiment, for the devices included in the system, the setting manners may be set according to an example of the embodiment, for example:
and the secondary classification device is used for randomly extracting 10% of pixels with smaller values between the water body pixels and the land pixels from the water body range and the land range of the single-phase amphibious binary image extraction result, taking the union of the pixels extracted from the water body range and the land range as a secondary classification calculation object, repeating the steps of S24-S27 and S3-S4, and regenerating the single-phase amphibious binary image extraction result.
As a further implementation manner of this embodiment, the step of removing shadows by the multi-source multi-temporal method may be encapsulated as a physical/virtual device, so as to implement specific functions:
and the optical image data acquisition device is used for acquiring optical image data closest to the dual-polarized SAR image data in time.
A first shadow removing device for extracting an optical amphibious map result from the optical image data by adopting an optical remote sensing image vegetation and water body automatic extraction method, wherein the result is actually a binary map (the water body value is 1, the land value is 0) and is marked as B 2 The intersection of the single-phase amphibious map extraction result and the optical amphibious map result is defined as the combined binary map result (i.e. B 1 1U B 2 1) to obtain a further binary image, designated B 3 Extracting result B from single-phase amphibious binary image 1 Result B of optical amphibious map 2 Merging binary image results B 3 Performing grid vector conversion processing to obtain three vector images V respectively 1 、V 2 And V 3 Obtaining a vector diagram V having an intersecting relation with the vector diagram V3 1 Vector diagram V' of all entities in (a) 1 。
Second shadow removing means for, for vector image V 3 Is configured to perform the following processing: let the current entity be E 3 Computing entity E 3 Is of the area A 3 Calculating to obtain a entity E 3 Vector diagram V with intersecting relationship 1 Corresponding entity E in' 1 Is of the area A 1 Calculating to obtain a entity E 3 Vector diagram V with intersecting relationship 2 Corresponding entity E in (3) 2 Is of the area A 2 Definition A 1 And A 2 A smaller value of A s Definition A 1 And A 2 A larger value of A b Definition of inclusion degree inc=a 3 /A s Definition of the degree of expansion as exp=a b /A s If INC<0.8 or EXP>500%, then entity E 1 From vector diagrams V 1 And deleted.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the essence of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. The amphibious boundary extraction method for the dual-polarized SAR image is characterized by comprising the following steps of:
s1, acquiring dual-polarized SAR image data of a target area;
s2, counting two types of unipolar SAR image data contained in the bipolar SAR image data, respectively obtaining frequency histograms of first-type polarized data and second-type polarized data, extracting a first water peak value w1, a first land peak value l1 and a first valley value v1 from the frequency histograms of the first-type polarized data, and extracting a second water peak value w2, a second land peak value l2 and a second valley value v2 from the frequency histograms of the second-type polarized data; the step S2 is specifically as follows: s21, dividing the first type of polarization data/the second type of polarization data into blocks by taking preset pixels as units to form a grid formed by M x N image blocks, and merging pixels which are less than the preset pixels into the last image block; s22, removing background values in the image blocks to obtain effective values, and extracting histogram peak-valley values of the effective values one by one; s23, randomly extracting 10% of pixels in the effective value to form a set S; s24, sorting the data of the set S from small to large, taking 1% quantile of the sorted set S as the minimum value of the data range, and taking 99% quantile of the sorted set S as the maximum value of the data range; s25, equally dividing the data range into 256 intervals, and calculating the number of pixels falling into each interval to obtain a frequency histogram of the first type polarization data/the second type polarization data; s26, traversing 256 intervals of the frequency histogram, defining a target interval as the first water body peak value w 1/the second water body peak value w2 if the pixel value of the current target interval appears for the first time as the maximum value in the 9 nearest intervals, and defining a target interval as the first land peak value l 1/the second land peak value l2 if the pixel value of the current target interval appears for the second time as the maximum value in the 9 nearest intervals; s27, recording the section in which the minimum value is located in all sections between the first water peak value w1 and the first land peak value l 1/the second water peak value w2 and the second land peak value l2 as the first valley value v 1/the second valley value v2;
s3, taking the first type of polarization data as an X axis, and taking the second type of polarization data as a Y axis, establishing a Cartesian coordinate system, and marking three characteristic points of W (W1, W2), L (L1, L2) and V (V1, V2);
s4, a straight line k1 is formed by passing the W point and the L point, a straight line k2 perpendicular to the straight line k1 is formed by passing the V point, the dual-polarized data point falling on the left lower side of the straight line k2 is divided into a water body, the dual-polarized data point falling on the right upper side of the straight line k2 is divided into land, and a single-phase amphibious map extraction result is generated.
2. The amphibious boundary extraction method of dual polarized SAR image according to claim 1, further comprising, if the number difference between the water body pixel and the land pixel contained in the single phase amphibious binary image extraction result is more than two times, performing a secondary classification.
3. The amphibious boundary extraction method of dual polarized SAR image according to claim 2, wherein said secondary classification comprises randomly extracting 10% of the number of smaller pixels between the water body pixels and the land pixels from within the water body range and the land range of the single phase amphibious map extraction result, taking the union of the pixels extracted from the water body range and the land range as the secondary classification calculation object, repeating the steps of S24-S27 and S3-S4, and regenerating the single phase amphibious map extraction result.
4. A amphibious boundary extraction method of dual polarized SAR image as claimed in claim 1 or 3, further comprising obtaining optical image data closest in time to said dual polarized SAR image data, extracting optical amphibious map result from said optical image data by using optical remote sensing image vegetation and water automatic extraction method, and combining said optical amphibious map result with said optical image dataThe intersection of the single-phase amphibious binary image extraction result and the optical amphibious binary image result is defined as a combined binary image result, and the single-phase amphibious binary image extraction result, the optical amphibious binary image result and the combined binary image result are subjected to grid vector conversion processing to respectively obtain three vector images V 1 、V 2 And V 3 Obtaining a vector diagram V having an intersecting relation with the vector diagram V3 1 A vector diagram V' 1 of all entities in the (c).
5. The amphibious boundary extraction method of dual polarized SAR image according to claim 4, further comprising, for vector diagram V 3 Is configured to perform the following processing: let the current entity be E 3 Computing entity E 3 Is of the area A 3 Calculating to obtain a entity E 3 Corresponding entity E in vector diagram V' 1 with intersecting relationship 1 Is of the area A 1 Calculating to obtain a entity E 3 Vector diagram V with intersecting relationship 2 Corresponding entity E in (3) 2 Is of the area A 2 Definition A 1 And A 2 A smaller value of A s Definition A 1 And A 2 A larger value of A b Definition of inclusion degree inc=a 3 /A s Definition of the degree of expansion as exp=a b /A s If INC<0.8 or EXP>500%, then entity E 1 Deleted from the vector diagram V' 1.
6. The amphibious boundary extraction method of dual polarized SAR image according to claim 1, further comprising preprocessing the dual polarized SAR image data of the acquired target area after the dual polarized SAR image data.
7. An amphibious boundary extraction system for dual polarized SAR images, comprising:
the data acquisition device is used for acquiring dual-polarized SAR image data of the target area;
the data analysis device is used for counting two types of unipolar SAR image data contained in the bipolar SAR image data, respectively obtaining frequency histograms of first-type polarized data and second-type polarized data, extracting a first water body peak value w1, a first land peak value l1 and a first valley value v1 from the frequency histograms of the first-type polarized data, and extracting a second water body peak value w2, a second land peak value l2 and a second valley value v2 from the frequency histograms of the second-type polarized data; dividing the first type polarization data/the second type polarization data into blocks by taking preset pixels as units to form a grid formed by M.N image blocks, and merging pixels which are less than the preset pixels into the last image block; s22, removing background values in the image blocks to obtain effective values, and extracting histogram peak-valley values of the effective values one by one; s23, randomly extracting 10% of pixels in the effective value to form a set S; s24, sorting the data of the set S from small to large, taking 1% quantile of the sorted set S as the minimum value of the data range, and taking 99% quantile of the sorted set S as the maximum value of the data range; s25, equally dividing the data range into 256 intervals, and calculating the number of pixels falling into each interval to obtain a frequency histogram of the first type polarization data/the second type polarization data; s26, traversing 256 intervals of the frequency histogram, defining a target interval as the first water body peak value w 1/the second water body peak value w2 if the pixel value of the current target interval appears for the first time as the maximum value in the 9 nearest intervals, and defining a target interval as the first land peak value l 1/the second land peak value l2 if the pixel value of the current target interval appears for the second time as the maximum value in the 9 nearest intervals; s27, recording the section in which the minimum value is located in all sections between the first water peak value w1 and the first land peak value l 1/the second water peak value w2 and the second land peak value l2 as the first valley value v 1/the second valley value v2;
the data modeling device is used for establishing a Cartesian coordinate system by taking the first type of polarization data as an X axis and the second type of polarization data as a Y axis, and marking three characteristic points W (W1, W2), L (L1, L2) and V (V1, V2);
the data extraction device is used for making a straight line k1 through a W point and an L point, making a straight line k2 perpendicular to the straight line k1 through a V point, dividing a dual-polarized data point falling on the left lower side of the straight line k2 into a water body, dividing a dual-polarized data point falling on the right upper side of the straight line k2 into a land, and generating a single-phase amphibious binary image extraction result.
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