A Study on the Unsupervised Change Detection for Hyperspectral Data Using Similarity Measure Techniques

화소간 유사도 측정 기법을 이용한 하이퍼스펙트럴 데이터의 무감독 변화탐지에 관한 연구

In this paper, we propose the unsupervised change detection algorithm that apply the similarity measure techniques to the hyperspectral image. The general similarity measures including euclidean distance and spectral angle were compared. The spectral similarity scale algorithm for reducing the problems of those techniques was studied and tested with Hyperion data. The thresholds for detecting the change area were estimated through EM(Expectation-Maximization) algorithm. The experimental result shows that the similarity measure techniques and EM algorithm can be applied effectively for the unsupervised change detection of the hyperspectral data.