CN110264474B - Water-land segmentation method of SAR remote sensing image - Google Patents
Water-land segmentation method of SAR remote sensing image Download PDFInfo
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Abstract
An amphibious segmentation method of SAR remote sensing images belongs to the technical field of amphibious segmentation of SAR remote sensing images. The method solves the problems of low segmentation accuracy and low segmentation efficiency of the existing land and water segmentation method. The method comprises the steps of utilizing the rectangular window to sequentially slide on the whole SAR remote sensing image, calculating the region consistency in the rectangular window to roughly extract the water body candidate region, further screening the water body candidate region by utilizing the area of the water body candidate region and the overlapping degree of the rectangular window, removing the false detection region, and finally obtaining the land and water segmentation result of the SAR remote sensing image. The method can be applied to the technical field of the land and water segmentation of SAR remote sensing images.
Description
Technical Field
The invention belongs to the technical field of land and water segmentation of SAR remote sensing images, and particularly relates to a land and water segmentation method of an SAR remote sensing image.
Background
Synthetic Aperture Radar (SAR) is an active microwave imaging sensor. Compared with passive sensors such as optical sensors, infrared sensors and the like, the imaging of the sensor is not influenced by various conditions such as weather, illumination and the like, and the sensor has the remarkable advantages of all-weather and vegetation penetration and the like. The SAR image is used for monitoring the water body area, the fields of fishery management, marine pollution monitoring and the like are facilitated, and further processing and analysis such as ship detection, monitoring, identification and the like can be carried out. Therefore, the SAR image land and water segmentation taking the extracted water area as the target has important significance.
In the SAR image land and water segmentation, a water body candidate region is usually extracted by using the existing image segmentation method, and false detection such as shadow elimination by considering other characteristics is considered, so that land and water segmentation precision is improved. Common methods for SAR image land-water segmentation that researchers have proposed are threshold segmentation methods, including Otsu threshold segmentation, multi-threshold land-water segmentation based on Otsu, and the like. The key of the threshold segmentation method is the determination of the threshold, and results such as over-segmentation and under-segmentation are easy to generate if the threshold is not properly selected. Although the methods are superior to the traditional threshold segmentation result, complete self-adaptive detection cannot be realized on the rough water body surface, and water body information cannot be correctly segmented from the SAR image with a large number of shadows.
Besides threshold segmentation, the SAR image land-water segmentation method further comprises land-water segmentation method which utilizes an active contour model algorithm to detect a coastline, land-water segmentation based on Parzen window probability estimation and a level set CV model, combination of level set theory and multi-scale analysis and the like. Although the active contour model algorithm can better solve the problem caused by the rough surface of the water body than the threshold segmentation algorithm, the active contour model algorithm is a semi-automatic detection algorithm, when land and water are segmented, a seed point needs to be initialized manually and appropriate algorithm parameters need to be set, so that the adaptability of the algorithm is poor, and meanwhile, the calculation efficiency of the method is low for the image with a large breadth.
In addition, the above conventional land and water segmentation methods based on pixels do not fully utilize the structural texture features of the land feature elements, and thus the accuracy of land and water segmentation results is low.
Disclosure of Invention
The invention aims to solve the problems of low segmentation accuracy and low segmentation efficiency of the existing land and water segmentation method.
The technical scheme adopted by the invention for solving the technical problems is as follows: an amphibious segmentation method of SAR remote sensing images comprises the following steps:
the method comprises the following steps that firstly, a rectangular window is used for sliding on the whole remote sensing SAR image in sequence, and the rectangular window needs to traverse the whole SAR remote sensing image; setting the length and the width of a rectangular window as M and N respectively, and setting the sliding step length of the rectangular window as d;
step two, calculating the region consistency in the rectangular window at each sliding position, and realizing the rough selection of the water body candidate region in the SAR remote sensing image;
step three, in the roughly selected water body candidate region, finely selecting the roughly selected water body candidate region according to the area size of each sub candidate region to obtain a finely selected water body candidate region;
and step four, extracting a rectangular window overlapping image of the selected water body candidate area, obtaining a finally determined water body area according to the rectangular window overlapping degree of each sub-candidate area in the overlapping image, and completing land and water segmentation of the SAR remote sensing image.
The invention has the beneficial effects that: the invention relates to a land and water segmentation method of an SAR remote sensing image, which comprises the steps of sliding a rectangular window on the whole SAR remote sensing image in sequence, calculating the region consistency in the rectangular window to carry out rough extraction on a water body candidate region, further screening the water body candidate region by using the area of the water body candidate region and the overlapping degree of the rectangular window, removing a false detection region, and finally obtaining a land and water segmentation result of the SAR remote sensing image.
Drawings
FIG. 1 is a flow chart of an amphibious segmentation method of SAR remote sensing images of the invention;
FIG. 2 is a first raw SAR remote sensing image;
fig. 3 is a diagram of the result of automatic land and water segmentation of a first raw SAR remote sensing image;
FIG. 4 is a second raw SAR remote sensing image;
fig. 5 is a diagram of the results of automatic land-water segmentation of a second raw SAR remote sensing image;
FIG. 6 is a graph of the effect of segmentation when the size of the rectangular window is 30 × 30 at a certain sliding step;
FIG. 7 is a graph of the effect of segmentation when the rectangular window has a size of 120 × 120 with a fixed sliding step;
FIG. 8 is a graph showing the effect of segmentation when the sliding step size is 6 for a certain rectangular window;
fig. 9 is a graph showing the effect of division when the sliding window is constant and the sliding step is 40.
Detailed Description
The first embodiment is as follows: as shown in fig. 1, the method for segmenting the SAR remote sensing image in the water and land according to the embodiment includes the following steps:
the method comprises the following steps that firstly, a rectangular window is used for sliding on the whole SAR remote sensing image in sequence, and the rectangular window needs to traverse the whole SAR remote sensing image; setting the length and the width of a rectangular window as M and N respectively, and setting the sliding step length of the rectangular window as d;
step two, calculating the region consistency in the rectangular window at each sliding position, and realizing the rough selection of the water body candidate region in the SAR remote sensing image;
step three, in the roughly selected water body candidate region, finely selecting the roughly selected water body candidate region according to the area size of each sub candidate region to obtain a finely selected water body candidate region;
and step four, extracting a rectangular window overlapping image of the selected water body candidate area, obtaining a finally determined water body area according to the rectangular window overlapping degree of each sub-candidate area in the overlapping image, and completing land and water segmentation of the SAR remote sensing image.
The land and water segmentation method based on the region considers the spatial position relation of adjacent pixel points, and can overcome the defects of the land and water segmentation method based on the pixels to a certain extent. According to the SAR remote sensing image land and water segmentation method based on the region, firstly, the candidate region of the water body is extracted by using the visual features of the region, and then the false detection is removed by using the post-processing technology.
Meanwhile, the hole part in the water area detected by the embodiment is a non-water target such as a ship, an island and the like, so the embodiment can also be used for detecting the non-water target such as a water ship and the like.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the specific process of the second step is as follows:
the calculation formula of the area uniformity Con in the rectangular window is as follows:
wherein: x is the number ofijRepresenting the gray value at pixel point (i, j) in the rectangular window, i is 1,2, …, M, j is 1,2, …, N, the intermediate variable f (x)ij) The calculation formula of (a) is as follows:
wherein: x represents the mean value of the gray values of all the pixel points in the rectangular window, and s is a constant;
x can also be set as the upper left pixel point X in the rectangular window11The gray level of the pixel position and the gray level median of all the pixels in the rectangular window are generally set to be gray level mean values or median values, and the effect is good. s is a threshold value for determining the difference between two values, and can be generally set to 10.
If the region uniformity Con > T within the rectangular window at a certain sliding position1If not, the area in the rectangular window at the sliding position is not the water body candidate area;
wherein: t is1The SAR remote sensing image is generally set to be 0.7 for the region consistency threshold value;
and the corresponding water body candidate areas at all the sliding positions jointly form a roughly selected water body candidate area. The roughly selected water body candidate area is obtained by a union set of the communicated detected rectangular window areas, namely the roughly selected water body candidate area can be regarded as being composed of a plurality of sub water body candidate areas.
The third concrete implementation mode: the second embodiment is different from the first embodiment in that: the specific process of the third step is as follows:
the area of the water body area is relatively large, and the area of the false detection area is relatively small. According to the method, partial false detection regions can be removed according to the area size of each sub water body candidate region.
If the proportion of the area of a certain sub-candidate region in the whole SAR remote sensing image is smaller than the threshold S1If the area of the block of sub-candidate region accounts for the whole SAR remote sensing image, judging that the block of sub-candidate region is a land region, and if the area of the block of sub-candidate region accounts for the whole SAR remote sensing image, the area of the block of sub-candidate region is larger than or equal to a threshold S1Judging that the block of sub-candidate area is a water body area;
and similarly, judging whether the other block sub-candidate regions are water body regions or not, and taking all the judged water body regions as the selected water body candidate regions.
The proportion of the area of a certain block of sub-candidate region in the whole SAR remote sensing image is the proportion of the pixel number of the certain block of sub-candidate region in the pixel number of the whole SAR remote sensing image.
The fourth concrete implementation mode: the third difference between the present embodiment and the specific embodiment is that: the specific process of the step four is as follows:
the rectangular window overlapping degree of each pixel point position in the rectangular window overlapping graph is as follows:
wherein, Overlap (i, j) is the rectangular window overlapping degree of the position of the pixel point (i, j) in the rectangular window overlapping graph, K is the total number of the rectangular windows passing through the pixel point (i, j) in the sliding process, and wkRepresenting the kth rectangular window passing through the pixel point (i, j) in the sliding process;
if wkThe region consistency in the inner region satisfies Con > T1Then the intermediate variable g (w)k) Takes 1, otherwise, g (w)k) The value of (A) is 0;
the overlapping degree of the rectangular windows of the nth sub-candidate region in the selected water body candidate region
Is defined as: the sum of the rectangular window overlapping degrees of all pixel point positions in the nth sub candidate region;
wherein S isnRepresents the nth sub-candidate region of the selected candidate regions of the water body,
is SnThe area of (d);
t is a rectangular window overlapping degree threshold value, w is the size of a rectangular window, alpha is an empirical parameter, and the value range of alpha is [0,1 ];
judgment of S by the formula (4)nWhether the water body area exists or not; and similarly, judging whether other sub-candidate areas in the selected water body candidate area are water body areas or not, and taking all the judged water body areas as finally determined water body areas.
The window overlapping degree of the water body candidate area provided by the embodiment describes the density degree of the sliding window in the area, and generally, the texture of the water body area in the SAR remote sensing image is smooth, and the gray levels of pixels in the water body area tend to be consistent. The detected sliding windows of the water body area are dense, and the overlapping degree is high; and the land area has complex texture and poor local consistency, the sliding window of the detected water body is sparse, the overlapping performance is poor, and the false detection can be judged. Therefore, the false detection area can be eliminated according to the window overlapping degree.
Fig. 2 and 4 are first and second raw SAR remote sensing images, respectively, and fig. 3 and 5 are automatic land-water segmentation result graphs of fig. 2 and 4, respectively, correspondingly.
In practice, the rectangular window size dictates the length and width of the rectangular sliding window. When the rectangular window is small, the detection precision of the water body edge is high, but the time for traversing the whole SAR remote sensing image by the rectangular window is long, and the calculation efficiency is low; when the rectangular window is large, the water body edge tends to be squared.
The results of land and water segmentation for different rectangular window sizes are shown in fig. 6 and 7, with the rectangular window size of fig. 6 being 30 × 30 and the rectangular window size of fig. 7 being 120 × 120. The sliding step specifies the number of pixels per movement of the rectangular window. The larger the sliding step length is, the faster the detection speed is, but the lower the precision is; the smaller the sliding step, the slower the detection speed, but the higher the accuracy. Comparison of land and water segmentation results for different slide steps as shown in fig. 8 and 9, the slide step of fig. 8 is 6, and the slide step of fig. 9 is 40. Therefore, the values of the two parameters, i.e., the size of the rectangular window and the sliding step, need to be selected by comprehensive consideration.
The above-described calculation examples of the present invention are merely to explain the calculation model and the calculation flow of the present invention in detail, and are not intended to limit the embodiments of the present invention. It will be apparent to those skilled in the art that other variations and modifications of the present invention can be made based on the above description, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed, and all such modifications and variations are possible and contemplated as falling within the scope of the invention.
Claims (1)
1. An amphibious segmentation method of SAR remote sensing images is characterized by comprising the following steps:
the method comprises the following steps that firstly, a rectangular window is used for sliding on the whole SAR remote sensing image in sequence, and the rectangular window needs to traverse the whole SAR remote sensing image; setting the length and the width of a rectangular window as M and N respectively, and setting the sliding step length of the rectangular window as d;
step two, calculating the region consistency in the rectangular window at each sliding position, and realizing the rough selection of the water body candidate region in the SAR remote sensing image; the specific process comprises the following steps:
the calculation formula of the area uniformity Con in the rectangular window is as follows:
wherein: x is the number ofijRepresentsGray value at pixel point (i, j) in the rectangular window, i is 1,2, …, M, j is 1,2, …, N, intermediate variable f (x)ij) The calculation formula of (a) is as follows:
wherein: x represents the mean value of the gray values of all the pixel points in the rectangular window, and s is a constant;
if the region uniformity Con > T within the rectangular window at a certain sliding position1If not, the area in the rectangular window at the sliding position is not the water body candidate area;
wherein: t is1Is a region consistency threshold;
the corresponding water body candidate areas at all the sliding positions jointly form a roughly selected water body candidate area;
step three, in the roughly selected water body candidate region, finely selecting the roughly selected water body candidate region according to the area size of each sub candidate region to obtain a finely selected water body candidate region; the specific process comprises the following steps:
if the proportion of the area of a certain sub-candidate region in the whole SAR remote sensing image is smaller than the threshold S1If the area of the block of sub-candidate region accounts for the whole SAR remote sensing image, judging that the block of sub-candidate region is a land region, and if the area of the block of sub-candidate region accounts for the whole SAR remote sensing image, the area of the block of sub-candidate region is larger than or equal to a threshold S1Judging that the block of sub-candidate area is a water body area;
similarly, judging whether other block sub-candidate regions are water body regions or not, and taking all the judged water body regions as the selected water body candidate regions;
extracting a rectangular window overlapping image of the selected water body candidate area, obtaining a finally determined water body area according to the rectangular window overlapping degree of each sub-candidate area in the overlapping image, and completing land and water segmentation of the SAR remote sensing image; the specific process comprises the following steps:
the rectangular window overlapping degree of each pixel point position in the rectangular window overlapping graph is as follows:
wherein, Overlap (i, j) is the rectangular window overlapping degree of the position of the pixel point (i, j) in the rectangular window overlapping graph, K is the total number of the rectangular windows passing through the pixel point (i, j) in the sliding process, and wkRepresenting the kth rectangular window passing through the pixel point (i, j) in the sliding process;
if wkThe region consistency in the inner region satisfies Con > T1Then the intermediate variable g (w)k) Takes 1, otherwise, g (w)k) The value of (A) is 0;
the overlapping degree of the rectangular windows of the nth sub-candidate region in the selected water body candidate region
Is defined as: the sum of the rectangular window overlapping degrees of all pixel point positions in the nth sub candidate region;
wherein S isnRepresents the nth sub-candidate region of the selected candidate regions of the water body,
is SnThe area of (d);
t is a rectangular window overlapping degree threshold value, w is the size of a rectangular window, alpha is an empirical parameter, and the value range of alpha is [0,1 ];
judgment of S by the formula (4)nWhether the water body area exists or not; similarly, other sub-regions in the selected water body candidate region are judgedAnd (4) judging whether the candidate area is a water body area or not, and taking all the judged water body areas as finally determined water body areas.
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| "高分辨率遥感影像道路网提取及变化检测";罗扬理;《中国优秀硕士学位论文全文数据库 信息科技辑》;20180615(第06期);第14，19，26-27，29，37-38页 * |
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