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基于两时相图像联合分类的SAR图像变化检测

期刊名称：红外与毫米波学报, 2009, 28(6): 466-471. (SCI, EI收录)
时间：0
分类：TP751[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
作者机构：[1]西安电子科技大学智能信息处理研究所和智能感知与图像理解教育部重点实验室,陕西西安710071
相关基金：国家自然科学基金（No.60672126,60673097,60702062,60803097）;国家863项目（No.2007AA122136,2007AA12Z223,2009AA12Z210）;教育部长江学者和创新团队支持计划资助（PCSIRT,IRT0645）;陕西省自然科学基金（No.2007F09）;国家教育部重点项目（No.108115）
相关项目：半监督免疫谱聚类及其在SAR图像理解中的应用

关键词：变化检测, SAR图像, 联合分类, 相似度, change detection, SAR image, joint-classification, similarity

中文摘要：

传统分类后比较法（post-classification comparison,PCC）存在分类累积误差问题,且对单幅图像分类精度要求较高,对此,根据不同时相图像的不变信息所具有的相关性,提出了一种基于两时相图像联合分类的SAR图像变化检测方法.该方法以灰度值作为输入信息,通过相似度计算可得两时相图像对应位置像素的灰度相似度,然后求解全局相似度阈值,并用于控制基于K-均值的联合分类器对两时相图像进行联合分类,最后通过类别比较获得变化检测结果.实验结果表明本文方法不但可提高单幅图像的分类精度,而且能够精确地把不同时相图像的不变地物信息划分为同一类别,减少了分类累积误差的影响,提高了变化检测性能.

英文摘要：

Since the classical post-classification comparison（PCC） technique was affected hy a significant cumulative error and high classification precision was needed for single image, a change-detection method based on joint-classification of bi-temporal SAR images was presented according to the correlation of the unchanged information in different temporal images. The proposed method took gray-levels as an input. The similarity of gray-levels relating to two pixels at the corresponding position for bi-temporal images was obtained through similarity operator. Then the global threshold value of similarity was got, which was used to control the joint-classifier based on K-means to classify the bi-temporal images. Finally, The change-detection map was produced by comparing with both classified images. Experimental results confirm that the proposed method not only improves the precision of classification for single image but also accurately classifies the unchanged geographical information in different temporal images into the same class. The proposed method reduces the influence of the cumulative error and improves the performance of change detection.