سابِق|تال

SAR image change detection method of DSSRM based on cascade segmentation

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

  • Email：jlzhang@mail.xidian.edu.cn
  • Introduction：张建龙(1976—　)，男，副教授，研究方向为遥感图像处理、模式识别理论和应用。E-mail：jlzhang@mail.xidian.edu.cn
ZHANG Jianlong,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

WANG Bin

ملخص

Synthetic Aperture Radar (SAR) images have region homogeneity with gray and texture. Considering that SRM (Statistical Region Merging) algorithms of image segmentation are efficient, stable and robust against noise, we propose a novel change detection method based on cascade segmentation with Dynamic Sorting Statistical Region Merging (DSSRM) algorithm. Firstly a DSSRM algorithm based on dynamic sorting is proposed to overcome conventional SRM's over-segmentation problem caused by single feature and static sorting. This algorithm takes the Manhattan distance of multi-feature of regions to be merged as the sorting criterion, and updates the adjacency matrix after each merging. Secondly based on the rule of minimizing mutual information we design a multi-channel complementary appearance model to improve the capability of constraint for region merging. Finally we present a cascade change detection framework with multiple levels. The first level projects difference image to super-pixel space via SRM, the second level utilizes DSSRM to dynamically merge regions; and the third level leverages a simplified SRM to realize region merging again to obtain final change detection map. Experimental results of the proposed method and proposed methods based on PCA and MRF are presented. By analysis and quantitative comparisons, the false alarm number and total of error number by DSSRMare decreased thereby the performance of KAPPA can get higher than methods based on PCA and MRF. DSSRM method is based on dynamic sorting algorithm with Manhattan distance of multi-feature of regions, it makes similar regions to be merged firstly. Experiments on construction of multi-channel illustrates that the more is the difference between channels the better is the performance of change detection. Our method improved the performance of SRM algorithm to avoid the over-segmentation phenomenon. Comparison experiments show that this method can obtain better performance of change detection than conventional SRM and state of art algorithms.

مفهوم

SAR image;change detection;MDSSRM;cascade segmentation

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