Remote Sensing Change Detection Based on Canonical Correlation Analysis and Contextual Bayes Decision

摘要

In this paper, we present a new approach combining canonical correlation analysis and contextual Bayes decision for change detection in bi-temporal multispectral remotely sensed images. Canonical correlation analysis in the form of Minimum Noise Fraction/Multivariate Alteration Detection transformation was first applied to two multispectral images to effectively yield a difference image, followed by a contextual Bayes decision procedure using automatic thresholding and Markov random field modeling techniques to identify areas where changes may have actually occurred from the difference image. An experiment of monitoring land reclamations in Hong Kong from Landsat TM/ETM+ images was conducted, and the results demonstrated effectiveness of our approach.