SAR image variation detecting method based on two-dimension gamma distribution

Abstract

The invention provides a method for detecting SAR image change based on two-dimensional Gamma distribution, comprising the following steps: according to the input SAR image to be detected and reference image data, the parameters of the two-dimensional Gamma distribution is estimated by a moment estimation method; likelihood ratio statistics are formed by Neyman-Pearson criterion; based on the two-dimensional distribution, clutter suppression is carried out according to the dependency of the image data, thus gaining the image after clutter suppression; CFAR normalization is carried out on the image after clutter suppression; furthermore, global thresholds are set so as to binarize the image, thus obtaining an initial detection result; the binary image after detection is processed morphologically, counting-filtered, and target-clustered so as to further eliminate isolated false alarm points, thus gaining the final detection result. The method reaches higher detection rate based on the low false alarm point, and is applicable to detect artificial objects under various clutter environments, more especially under the strong clutter environments.

Description

technical field [0001] The invention belongs to the field of SAR image processing, and relates to a SAR image change detection method based on two-dimensional Gamma distribution. Background technique [0002] Synthetic Aperture Radar (SAR) itself is an active sensor that uses microwave perception. With a certain penetrating detection capability, it can detect a certain depth of subsurface or other camouflaged or covered targets, which makes SAR have great application potential in target detection. [0003] In the environment with strong clutter, the single CFAR target detection technology is greatly restricted. With the maturity of high-resolution and short-period repeated observation technologies, change detection technology has developed rapidly, and change detection technology applied to target detection has also become an important research direction. In order to detect artificial targets in large scenes, the direct comparison detection method at the pixel level is a m...

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Problems solved by technology

[0004] In this kind of change detection method, although the commonly used image difference method or ratio method based on one-dimensional distribution model is simple and easy to implement, it cannot make full use of the correlation of image data in different phases; while using two-dimensional distribution model Among the detection methods, although the clutter suppression change detection method based on the two-dimensional Gaussian distribution uses image correlation for clutter suppression, SAR images generally do not simply obey the Gaussian distribution, so this method has the fitting accuracy of the distribution model problem; while the correlation coefficient change detection method based on two-dimensional Gamma distribution uses a distribution model with high fitting accuracy, but only uses the correlation coefficient as the basis for judgment, and it is difficult to set a reasonable detection threshold

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