Synthetic aperture radar (SAR) imagine change detection based on histogram and elite genetic clustering algorithm

Abstract

The invention relates to synthetic aperture radar (SAR) imagine change detection based on a histogram and an elite genetic clustering algorithm. The SAR imagine change detection is combined with global searching ability of a genetic algorithm and local searching ability of a fuzzy clustering algorithm, quickens convergence rate of the algorithms, achieves a better imagine change detection effect and effectively shortens calculating speed of the algorithm by adopting the histogram.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a change detection method, in particular to a SAR image change detection based on histogram and elite genetic clustering algorithm, which can be applied to the change detection of remote sensing images. Background technique [0002] Synthetic Aperture Radar (SAR) has the characteristics of high resolution, all-weather, and all-time, and can easily obtain images of the same area at different times. The change detection of SAR image is to compare the remote sensing images of the same area surface obtained at different times to obtain the difference map, and then use the gray value of the difference image to divide the image into changing regions and unchanged regions. Change detection in SAR images has a very wide range of applications in environmental monitoring, disaster estimation, land use, forest harvesting monitoring, and crop growth monitoring. [0003] The change ...

AI Technical Summary

Problems solved by technology

The change detection algorithm based on PCA and k-means clustering proposed by T. Celik in 2009 uses PCA to reduce the dimension of the difference map, and then uses k-means clustering, which greatly reduces the amount of computation, but due to the reduction Some information is lost in the process of dimensioning, which leads to large errors in the results

Although the SA-GKC algorithm improved on the basis of FCM and genetic algorithm proposed by A.Ghosh and N.S.Mishra in 2010 has achieved good results, the algorithm idea is more complicated due to the combination of multiple algorithms.

The improved RFLICM algorithm proposed by Gong Maoguo et al. in 2012 has obtained relatively accurate change detection results. However, in the process of clustering initialization, the RFLICM algorithm uses a random method to obtain the initial cluster center point, which leads to these algorithms being difficult for the initial clustering. The defect that the central point is very sensitive, it is easy to fall into a local optimum

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