CN110211068B - Sub-pixel precision SAR image waterline mapping method - Google Patents
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Abstract
The invention provides a sub-pixel precision SAR image waterline mapping method, which comprises the following steps: preprocessing an SAR image; filtering speckle noise of the SAR image; extracting a pixel-level water area; calculating and extracting sub-pixel level waterline points; and (5) fitting the sub-pixel level waterline points in a segmented manner to obtain the waterline with sub-pixel level precision. By the method, the water area contour line with sub-pixel level precision can be obtained, and the method has important significance for remote sensing water area distribution, measurement, evaluation and prediction.
Description
Technical Field
The invention relates to the technical field of Synthetic Aperture Radar (SAR) image water area detection, in particular to a sub-pixel precision SAR image waterline mapping method.
Background
Surface water resources are more and more widely concerned, and surface water is an important carrier for land regional water circulation and has important influence on regional ecosystems. The effective research of water area information has important significance on agricultural irrigation, water resource investigation, flood disaster forecasting, early warning, monitoring and the like. Meanwhile, the method has guiding significance for comprehensive investigation and reasonable planning and use of water resources. The SAR serving as an active microwave sensor has the advantages of all-time, all-weather, remote earth observation and the like. Therefore, many researchers use the SAR image to perform segmentation and extraction on the surface water, so as to realize acquisition and calculation of the water area information. The land and water cut line can be obtained through the SAR image, but the technical difficulty of the SAR image is how to obtain the land and water cut line with higher precision at low spatial resolution SAR because the SAR image generally has lower spatial resolution.
Currently, some research has been made in the industry for SAR image waterline mapping technology. However, most of the algorithms work at a pixel level (such as a threshold-based SAR image segmentation method, a gradient-based edge detection method and a level set segmentation method), because the resolution of the SAR image is low, the boundary between different ground objects is not obvious, the conventional algorithms can only work at the pixel level, and in some application occasions, the obtained result is not accurate enough.
In summary, how to obtain a sub-pixel-level precision waterline on a low-resolution SAR image, and expand the application field and range have important significance.
Disclosure of Invention
The invention aims to provide a sub-pixel precision SAR image waterline mapping method, which is used for obtaining a waterline with sub-pixel precision on a low-resolution SAR image.
In order to achieve the above purpose, the invention provides the following technical scheme: a sub-pixel precision SAR image waterline mapping method comprises the following steps:
step 1, preprocessing an SAR image;
step 2, filtering speckle noise of the SAR image;
step 41, building SAW and saving related information
After acquiring a pixel-level water area, acquiring a pixel-level water area boundary line, discretizing a pixel-level waterline, acquiring the position of a pixel-level water area boundary point, establishing a 7 x 7 window by taking the point as a center, extracting and storing an image gray value in the window in a matrix Mi, and recording coordinate information of the pixel-level waterline point contained in the window;
step 42, obtain the new smoother SAW and the function expression corresponding to the pixel level waterline
Setting interpolation step length to be 0.1 by utilizing a bicubic spline interpolation algorithm, interpolating the matrix Mi to obtain a smoother matrix Mci, mapping coordinate points of a pixel level waterline in the window to a coordinate system corresponding to the matrix Mci, and fitting a functional relation f wl (x，y)；
Step 43, extracting sub-pixel level waterline in new SAW based on GAC algorithm
First, a level set function is initialized to
Where is a constant, τ 0 Is the area of the water area, f wl Is τ 0 I is the image domain.
The partial differential equation for the horizontal plane evolution is then evolved until the equation converges:
where g (x) is a stop function,
beta is a proportionality constant greater than 0;
step 44, mapping the sub-pixel level waterline to a coordinate system of the original image and storing the sub-pixel level waterline in a matrix;
step 45, judging whether pixel points of all pixel-level waterlines are traversed or not, and if so, exiting the circulation, otherwise, executing step 41 to step 44 in a circulating manner;
and 5, fitting the sub-pixel level waterline points in a segmented manner to obtain the waterline with sub-pixel level precision.
As can be seen from the analysis, the method comprises a plurality of steps: preprocessing an SAR image; filtering speckle noise of the SAR image; extracting a pixel-level water area; establishing an analysis window along the boundary of the pixel-level water area to calculate and extract a sub-pixel-level waterline point; and (5) fitting the sub-pixel level waterline points in a segmented manner to obtain the waterline with sub-pixel level precision. By the method and the device, the water area contour line with sub-pixel precision can be obtained, and the method and the device have important significance for remote sensing water area distribution, measurement, evaluation and prediction.
Drawings
FIG. 1 is a block flow diagram of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the present invention specifically includes the following steps: step 1, preprocessing is carried out on the SAR image, wherein geometric correction is carried out on the SAR image, then geocoding is carried out on the image, and finally normalization operation is carried out on the SAR image. And 2, filtering speckle noise, for example, filtering speckle noise of the SAR image by a Non-Local de-multiplexing on a Local Pixels Selected and Ratio Distance (NL-SPSRD for short) speckle filtering algorithm based on a Distance Ratio, and filtering the speckle noise in the SAR image by using the preprocessed SAR image and the NL-SPSRD filtering algorithm while keeping better edge information. And 3, extracting a pixel-level water area, preferably extracting the pixel-level water area based on a Gaussian fuzzy C-means (GFCM for short) algorithm of Gaussian filtering and water area distribution characteristics. The filtered SAR image is utilized, a GFCM algorithm is adopted to obtain a pixel-level water area, then a potential real water area is extracted according to the characteristic that the water area has connectivity, the interference of a non-water area in the SAR image is removed, and finally the boundary of the potential water area is sampled to obtain the pixel-level water area boundary. And 4, extracting the boundary points of the sub-pixel-level water area, and establishing an analysis Window (Subpixel Analyzing Window, SAW) along the boundary of the pixel-level water area based on a sub-pixel water line Mapping Method (SWMM) of a bicubic spline interpolation and a Geometric Active Contour (GAC for short) to calculate and extract the boundary points of the sub-pixel-level water area. Traversing each pixel point on the boundary of the pixel-level water area, establishing SAW (surface Acoustic wave) by taking the pixel points as the center, and calculating the position point of the new sub-pixel-level water area boundary by using an SWMM (Single-wall micro-mirror) method. And 5, fitting the sub-pixel level waterline points in a segmented manner to obtain the waterline with sub-pixel level precision. Because the same pixel point can be calculated in a plurality of adjacent SAW for a plurality of times, and a plurality of results can be calculated for a new waterline, a complete independent subpixel level waterline is obtained by adopting a piecewise curve fitting method.
More specifically, in step 1, specifically, multi-view processing is performed on Single Look Complex (SLC) SAR data, the azimuth direction and the range direction are both set to 10, and then geometric correction is performed on the multi-view processed data, so as to obtain an SAR amplitude image corresponding to the geographic position.
In step 2, due to the coherence phenomenon of radar irradiation, a large amount of speckle noise exists in the SAR image, and the speckle noise is multiplicative noise and can greatly affect the segmentation result. Although the non-local filtering algorithm is developed to a great extent, the invention provides a non-local NL-SPSRD speckle filtering algorithm to filter speckle noise in the SAR image.
The NL-SPSRD speckle filtering algorithm mainly comprises the following steps:
1) establishing a coherent speckle distribution model in the amplitude SAR image as follows:
for a pair of amplitude SAR images for L-view, the measurements usually conform to the Nakagami-ralleleigh model:
wherein A is s Is the amplitude value of the pixel s and,
with the estimated true amplitude value, Γ is the gamma function,
the calculation formula of (a) is as follows:
the pixel s in equation (2) is a non-local window W s Center of (A), P s (s, t) is a probability density function of pixel s and pixel t.
2) The correlation between two pixels is calculated by the formula:
for an amplitude SAR image of L vision, the method is used for measuring any two pixels A 1 ，A 2 The formula for calculating the Probability Density Function (PDF) of the correlation is as follows:
3) the formula for calculating the correlation between two pixels by introducing the ratio distance is as follows:
ratio Distance (RD) has been shown to be highly robust in describing features of SAR images, and the present invention expresses RD as r ═ a 1 /A 2 And introducing r into the formula (3) to obtain new P R Comprises the following steps:
4) the calculation formula introduced into the pixel matrix is:
due to the high degree of similarity and chance between adjacent pixels, a similarity matrix of size W is introduced into the calculation process, assuming each pixel in the matrix is independent. P s The calculation formula of (s, t) may be updated as:
5) the formula for using geometric means to represent the PDF is:
in general P s The value of (s, t) is small, and in order to avoid the decimal place of calculation error in the calculation process, the invention uses the geometric mean value of all the elements in the similarity matrix to represent P s (s, t) replacing the product of the similarity matrix, thus finally P s The expression of (s, t) is:
in step 3, pixel-level extraction is carried out on the water area in the SAR image based on a GFCM algorithm, which mainly comprises the following steps:
1) the objective function J is:
the image is represented as I rw Img (R, W), R, W respectively represent rows and columns, c cluster centers are arranged, and V ═ V (W) 1 ，v 2 …，v c ). The division of all pixels into c clusters is done by P iterations. The objective function J is:
element u in defined c x n two-dimensional membership matrix u, u irw Representing I in a picture rw Pixel point correspondence clustering v i Degree of membership.
2) Calculating membership u irw The formula is as follows:
3) performing Gaussian filtering on the membership matrix, and firstly establishing a filtering template, wherein the generation formula of the template M is as follows:
where k represents the template size, here equal to 3, and σ is a constant, here equal to 1.2.
Filtering each dimension of the two-dimensional membership matrix U by using the generated filtering template:
4) updating the clustering center V:
5) and (4) executing the steps 2, 3 and 4, judging whether the iteration times P are finished or not, and finishing the process when the iteration times P are finished.
In step 4, a more accurate extraction of sub-pixel level waterline will be performed based on the results of the above steps, and it is established that this is done under two assumed conditions: firstly, the periphery of a waterline of a pixel level comprises a water area and a land; and secondly, at the junction between the water area and the land, the backscattering coefficient of the radar generates jump.
The specific execution steps are as follows:
1) building SAW and saving related information
After the pixel-level water area is obtained, a pixel-level water area boundary line is obtained, a pixel-level waterline is discretized, and the position of the pixel-level water area boundary point is obtained. Taking the point as the center, establishing a 7 multiplied by 7 window, extracting the image gray value in the window and storing the image gray value in a matrix M i And recording coordinate information of pixel-level waterline points contained in the window.
2) Obtaining new smoother SAW and function expressions corresponding to pixel-level waterlines
Setting the interpolation step length to be 0.1 by utilizing a bicubic spline interpolation algorithm, and setting the matrix M to be i Interpolation is carried out to obtain a smoother matrix M ci To prepare theMapping of coordinate points of a pixel-level pipeline in a window to a matrix M ci Fitting a functional relation f in the corresponding coordinate system wl (x，y)。
3) Extraction of sub-pixel level waterline in new SAW based on GAC algorithm
First, the level set function is initialized as:
where is a constant, τ 0 Is the area of the water area, f wl Is τ 0 I is the image domain.
The level set partial differential equation is then evolved until the equation converges:
where g (x) is a stop function,
beta is a proportionality constant greater than 0.
4) And mapping the sub-pixel level waterline into a coordinate system of the original image and storing the coordinate system in a matrix.
5) And (4) sequentially executing the subdivision steps (1) to (4) in the step (4), judging whether pixel points of all pixel-level waterlines are traversed and completed, and quitting circulation if the pixel points are traversed and executing the subdivision steps (1) to (4) circularly if the pixel points are not traversed and completed.
In step 5, sub-pixel level waterline points are fitted in a segmented manner to obtain a waterline with sub-pixel level precision. After the step 4 is completed, because a plurality of overlapped areas are calculated, the obtained sub-pixel points have the phenomena of overlapping dislocation and the like, according to a piecewise curve fitting method, 14 pixels are taken as step lengths along the pixel level, the coordinate points of a sub-pixel level waterline within the range of 14 multiplied by 14 are counted, all the points are put into a corresponding coordinate system according to the slope of the pixel level waterline, a smooth continuous curve is obtained by utilizing a fitting function, and the curve is taken as a final sub-pixel level waterline.
The SAR image shooting method is implemented for testing SAR images of the Dangjiang reservoir main reservoir area shot by the China high-grade three-number satellite working in different modes at different times. The Danjiang mouth reservoir is a water source area of central line engineering of north-south water and south China and the largest artificial fresh water lake in Asia, obtains a sub-pixel-level water and land division line, and has important significance for reservoir water quantity management and planning. Through practical tests, the method can obviously show that the sub-pixel level waterline obtained by the method is more accurate in accuracy, and has more significance for waterline segmentation of the SAR image with low spatial resolution.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.
Claims (3)
1. A sub-pixel precision SAR image waterline mapping method is characterized by comprising the following steps:
step 1, preprocessing an SAR image;
step 2, filtering speckle noise of the SAR image;
step 3, acquiring a water area at a pixel level by utilizing the filtered SAR image and adopting a GFCM (Gaussian filtered model) algorithm, extracting a potential real water area according to the characteristic that the water area has connectivity, removing interference of a non-water area in the SAR image, and sampling the boundary of the potential real water area to obtain a pixel-level water area boundary;
step 4, establishing two assumed conditions: firstly, the periphery of a waterline of a pixel level comprises a water area and a land; secondly, at the junction between the water area and the land, the backscattering coefficient of the radar generates jump; and establishing SAW along the boundary of the pixel-level water area by the SWMM algorithm based on bicubic spline interpolation and GAC to calculate and extract the boundary point of the subpixel-level water area, which specifically comprises the following steps:
step 41, building SAW and saving related information
After acquiring a pixel-level water area, acquiring a pixel-level water area boundary line, discretizing a pixel-level waterline, acquiring the position of a pixel-level water area boundary point, establishing a 7 x 7 window by taking the point as a center, extracting and storing an image gray value in the window in a matrix Mi, and recording coordinate information of the pixel-level waterline point contained in the window;
step 42, obtain the new smoother SAW and the function expression corresponding to the pixel level waterline
Setting interpolation step length to be 0.1 by utilizing a bicubic spline interpolation algorithm, interpolating the matrix Mi to obtain a smoother matrix Mci, mapping the coordinate points of the pixel level waterline in the window to a coordinate system corresponding to the matrix Mci, and fitting a functional relation f wl (x，y)；
Step 43, extracting subpixel level waterline in new SAW based on GAC algorithm, first initializing level set function as
Where is a constant, τ 0 Is the area of the water area, f wl Is tau 0 I is the image domain;
the partial differential equation for the horizontal plane evolution is then evolved until the equation converges:
where g (x) is a stop function,
beta is a proportionality constant greater than 0;
step 44, mapping the sub-pixel level waterline to a coordinate system of the original image and storing the sub-pixel level waterline in a matrix;
step 45, judging whether pixel points of all pixel-level waterlines are traversed or not, and if so, exiting the circulation, otherwise, executing step 41 to step 44 in a circulating manner;
and 5, fitting the sub-pixel level waterline points in a segmented manner to obtain the waterline with sub-pixel level precision.
2. The sub-pixel-precision SAR image waterline mapping method according to claim 1, characterized in that in step 2, speckle noise filtering is performed on the SAR image based on NL-SPSRD speckle filtering algorithm, specifically comprising the following steps:
step 21, establishing a coherent speckle distribution model in the amplitude SAR image:
for an L-view amplitude SAR image, the measurements generally conform to the Nakagami-Ralyleigh model:
wherein A is s Is the amplitude value of the pixel s and,
with the estimated true amplitude value, Γ is the gamma function;
wherein the content of the first and second substances,
the calculation formula of (a) is as follows:
and step 22, calculating the correlation between the two pixels:
the pixel s in equation (2) is a non-local window W s Center of (A), P s (s, t) is a probability density function of pixel s and pixel t, for an L-view amplitude SAR image, for measuring any two pixels A 1 ，A 2 The calculation formula of the probability density function PDF of the correlation is as follows:
step 23, introducing a ratio distance to calculate the correlation between two pixels:
the ratio distance RD, denoted r ═ a, has proven to be highly robust in characterizing SAR images 1 /A 2 And introducing r into the formula (3) to obtain new P R Comprises the following steps:
step 24, introducing the calculation of the pixel matrix:
introducing a similarity matrix of size W, P, assuming that each pixel in the matrix is independent s The calculation formula of (s, t) may be updated as:
step 24, using the geometric mean to represent the PDF:
representing P by using the geometric mean of all elements in the similarity matrix s (s, t) replacing the product of the similarity matrix, thus finally P s The expression of (s, t) is:
3. the sub-pixel-precision SAR image waterline mapping method according to claim 1, wherein step 3 specifically comprises the following steps:
step 31, setting an objective function J:
the image is represented as I rw Img (R, W), R, W representing the row and column, respectivelyC cluster centers are set, V ═ V 1 ，v 2 …，v c ) Dividing all pixels into c clusters by P iterations, wherein an objective function J is as follows:
a defined c x n two-dimensional membership matrix U, the elements U in U irw Representing I in a picture rw The pixel points correspond to the membership degree of the cluster vi;
step 32, calculating the membership degree u irw The formula is as follows:
step 33, gaussian filtering the membership matrix:
firstly, a filtering template is established, and a template M generation formula is as follows:
where k represents the template size and is equal to 3, σ is a constant here equal to 1.2'
Filtering each dimension of the two-dimensional membership matrix U by using the generated filtering template:
step 34, updating the clustering center V
And step 35, judging whether the iteration times P are finished, finishing the process, and otherwise, circularly executing the steps 32, 33 and 34.
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| CN109389062A (en) * | 2018-09-14 | 2019-02-26 | 河南大学 | Utilize the method for High Resolution Spaceborne SAR image zooming-out lake land and water cut-off rule |
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| CN103377465A (en) * | 2013-03-21 | 2013-10-30 | 西安电子科技大学 | SAR image speckle reduction method based on sketch and kernel selection |
| CN109389062A (en) * | 2018-09-14 | 2019-02-26 | 河南大学 | Utilize the method for High Resolution Spaceborne SAR image zooming-out lake land and water cut-off rule |
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