Markov random field (MRF) iteration-based synthetic aperture radar (SAR) unsupervised change detection method and device

Abstract

The invention provides a markov random field (MRF) iteration-based synthetic aperture radar (SAR) unsupervised change detection method to overcome the defects of low detection capacity, accuracy and speed in the conventional SAR detection technology. Statistical parameters of a mixed distribution model of a difference map are estimated stably by an improved expectation-maximization (EM) algorithm, independency among pixels is defined by using an MRF model, and a more reliable and accurate image change detection result is generated by map cut-based MRF division. The invention also provides a device based on the method. The device comprises an optical fiber link input module, a digital signal processor (DSP), a synchronous dynamic random access memory (SDRAM), a compact peripheral component interconnect (CPCI) output module and a display terminal; the method and the device have the advantages that various high-accuracy high-speed unsupervised change detection can be realized; the device has the engineering application capacity of batch processing of mass data and meets the image analysis requirement under the complex condition; and compared with the conventional method, the method has the advantages that the processing accuracy and the processing speed are improved obviously.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image data processing, and in particular relates to a non-supervised change detection method of radar images. Background technique [0002] Synthetic Aperture Radar (SAR) image change detection technology is a technology to detect surface changes based on different time-phase images acquired by airborne or spaceborne SAR. In recent years, SAR image change detection technology has become a research hotspot at home and abroad. Optical data is affected by factors such as climate, coverage, etc., and cannot meet all change detection needs. As an active microwave sensor, SAR has all-weather, all-day, and strong penetration capabilities. It is of great significance to use SAR images for change detection. At present, SAR image change detection has been widely used in many fields, such as land use analysis, forest harvesting monitoring, disaster estimation, military reconnaissance, strike effect ...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the existing SAR image change detection technology cannot meet the needs of simultaneous detection of stronger and weaker regions, higher detection accuracy and speed, and real-time processing. Airport MRF (Markov Random Field) iterative SAR image unsupervised change detection method, the method uses the improved EM algorithm to robustly estimate the statistical parameters of the mixed distribution model of the difference map, uses the MRF model to define the dependencies between pixels, and uses the graph cut based The MRF segmentation of MRF produces more reliable and accurate image change detection results; the method of the present invention fully considers factors such as speckle noise and image statistical distribution, and solves the difficult problem of multi-category, high-precision, fast and robust unsupervised change detection

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