Segmentation combination-based adaptive constant false alarm rate target detection method for SAR image

Abstract

The invention discloses a segmentation combination-based adaptive constant false alarm rate target detection method for an SAR (synthetic aperture radar) image, and belongs to the technical field of synthetic aperture radars. The method comprises the following steps of: dividing a reference window into four sub windows, extracting uniformity statistics of the four sub windows and judging whether the sub windows are uniform; obtaining parameters for estimating background clutter models by adopting corresponding sub window combination strategies according to the non-uniform number, and then obtaining a detection threshold value by using a false alarm probability and a relationship between the clutter models; and comparing the pixel value of a current detection unit with the detection threshold value, judging whether a target exists, detecting the whole SAR image to be detected by adopting a running water form, and performing target fusion operation on the detected SAR image. According to the scheme, the method has low calculation quantity and simple operation, solves the problems of low detection probability, high false alarm rate and the like when the environment is complex and changeable and multiple targets are adjacent under high-resolution large scenes in the prior art, obviously improves the detection effect, and can keep good detection performance under various complex detection environments.

Description

technical field [0001] The patent of the present invention belongs to the technical field of synthetic aperture radar (SAR), and relates to a method for real-time detection of SAR image targets. Background technique [0002] In recent years, the rapid development of Synthetic Aperture Radar (SAR) has not only enhanced its military application, but also began to affect civilian applications such as daily terrain surveying, mapping, navigation and monitoring. However, as the image data obtained by airborne and spaceborne SAR sensors tends to be massive, and the existing image analysis capabilities cannot meet the processing requirements of the collected data, it is becoming more and more urgent to develop SAR image interpretation technology. SAR image interpretation technology is rich in content, and spreads a wide variety of branches for different applications. One of the important branches is how to find the region of interest (ROI) from the unpredictable background clutter...

AI Technical Summary

Problems solved by technology

[0007] Huang Xiang proposed an adaptive detection algorithm based on region classification (RC-CFAR algorithm) in 2005. This method can extract the environmental parameters of the unit to be detected by dividing the dimension and window of the scene data, but the window The combination idea is not perfect, and it will cause the loss of target detection rate in some more complex target scenarios

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