Integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method

Abstract

The invention provides an integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method, comprising the following steps of: (1) providing a G0 distribution-based self-adaptive global threshold CFAR pre-segmentation algorithm used for generating a target index matrix by combining the statistical property of data; (2) providing an integral image-based G0 distribution statistical parameter quick estimation method, wherein the statistical parameter can be calculated through simple operations such as addition and subtraction once 2-order and 4-prder integral images of an original image are obtained during the implementation of the method; and (3) giving out a basic implementation process of the ACCA-CFAR SAR image target detection method. Through the integral image-based G0 distribution statistical parameter quick estimation strategy provided by the invention, the time efficiency of the method can be greatly improved and the time complexity of the method is irrelevant to the size of a sliding window; and the requirement of the existing automatic target recognition (ATR) system on the treatment of large-scene data can be met to a great extent.

Description

technical field [0001] The invention relates to the technical field of SAR image interpretation, in particular to a fast ACCA-CFARSAR image target detection method based on integral images. Background technique [0002] The emergence of high-resolution, large-scene Synthetic Aperture Radar (SAR) images has made it possible to apply SAR images more widely, and at the same time it has brought new challenges to SAR image interpretation technology. , low resolution, small scene SAR image processing technology is no longer applicable. As one of the key SAR image interpretation technologies, object detection has a great influence on the performance and efficiency of subsequent processing such as feature extraction, object recognition and classification. At present, there has been some development in this area. Constant False Alarm Rate (CFAR) detection is the most widely used target detection method. Its basic principle is to estimate the energy of background clutter based on the...

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Problems solved by technology

[0010] (2) The global threshold pre-segmentation algorithm based on target confidence used in literature [6] does not consider the statistical characteristics of the data, and its performance only depends on the target confidence, that is, the ratio of the number of target pixels to the total number of pixels in the image, so The performance of the algorithm is sensitive to the value of this parameter

[0016] (2) Compared with G 0 distribution, the statistical parameters of the K-distribution and the calculation of the local CFAR threshold are complex

However, as described in [6], when the sliding window parameters are set to h=71, r=20, and the image size is 1375×1880, the running time of the algorithm is still 40.0471s, which shows that the algorithm is still far from satisfying Large scene data processing and application requirements of real-time ATR system

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