SAR (Synthetic Aperture Radar) image object detection method based on Primal Sketch algorithm

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image object detection method based on a Primal Sketch algorithm, mainly solving the problem that the traditional object detection method cannot realize the detection on different types of artificial objects. The implementing process of the SAR image object detection method based on the Primal Sketch algorithm comprises the following stepsof: (1) obtaining a line segment aggregation expressing image structure information by applying the Primal Sketch algorithm to original SAR images; (2) defining and calculating the regularity degree and the regularity ratio of all line segments in the line segment aggregation; (3) determining a seed line segment aggregation for region growing; (4) executing the region growing by taking the seed line segments as the reference to obtain a candidate object region aggregation including artificial objects and natural objects; and (5) screening according to the characteristics of the line segments in the candidate object region aggregation to obtain final artificial objects, namely bridges, ports and buildings. Compared with the prior art, the detection method disclosed by the invention has theadvantages of strong applicability, capability of realizing the detection on different types of artificial objects, exact detection result and low false alarm rate; and the invention is suitable for the SAR image object detection under the condition of multiple object types and different sizes of objects.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a SAR image target detection method, which can be used for target recognition and tracking in the fields of SAR image processing, computer vision, intelligent control and the like. Background technique [0002] The main task of target detection is to determine the position of the target of interest and realize the separation of the target from the background. When the target size is small, the difficulties of target detection mainly include: (1) the target has no information such as shape, size and texture, and the amount of available information is small; (2) when the SNR is low, the target is easily overwhelmed by noise; (3) If the target trajectory information accumulation of the image sequence is used, the storage capacity is large and the calculation speed is limited. [0003] SAR image target detection specifically refers to the image processing technology that uses...

AI Technical Summary

Problems solved by technology

[0005] (1) The calculation of the target detection process is large, the false alarm rate of the detection result is large, and the result of the target detection is more sensitive to noise;

[0006] (2) It has a great dependence on the prior information of the SAR image, such as the type of the target, the size of the target, and the statistical distribution model of the background clutter, and can only be used for a single type of artificial target such as a bridge or a port or a building. waiting for testing

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