基于小波域Fisher分类器的SAR图像变化检测

摘要

An unsupervised technique for detecting change area between two SAR images was proposed. The detection process is based on distribution property of the joint intensity histograms and need not distribution hypothesis. The algorithm uses adaptive edge detection to get training data. The joint intensity histograms in different levels are used to decide the membership degree of unlabeled points through Fisher classifier. The fusion model which considers the context relationship and inter-scale information improves the sensitivity. The simulation results of two real SAR images show that the algorithm is effective and has better detection results.%给出了一种无监督SAR图像变化检测算法,它不需要分布假设,而是通过联合灰度直方图的分布特性进行判别.算法利用自适应边缘检测提取训练数据,通过Fisher分类器对联合直方图进行判别分析,得到不同小波层待检测点隶属度,并根据邻域关系以及上下文进行融合,得到最终检测结果.对真实SAR图像进行检测,得到了较好的检测结果.