Change detection method for remotely sensed images basedon multivariate analysis method and statistical test

摘要

A new change detection method for remotely sensed images is proposed. This method can be applied to two images which have different number of spectral bands and/or have different spectral ranges. The proposed method converts two multi-spectral-multi-temporal images into two sets of canonical variate images which have limited correlation called the canonical correlation. Then, one or more canonical variate images which are the most suitable for change detection are selected and change detection regions in the original images are extracted by using statistical modeling and statistical test. In this paper, the detail of the proposed method is described. Some experiments using simulated multi-spectral-multi-temporal images based on spectral profiles in ASTER Spectral Library are conducted to confirm change detection accuracy. The experimental results show reasonable changed regions and their change quantities.