<title>Method for change analysis with weight of significance using multitemporal multispectral images</title>

ABSTRACT A new method is proposed for change analysis with weight of significance between two multi-temporal multi-spectral images.This method gives us areas which indicate the assigned temporal change, for example, from vegetation to bare soil. Image dataare projected onto a feature space in which the assigned change is emphasized, and temporal changes between two images aredetected with suppression ofirrelevant changes. The validity ofthe method is confirmed by numerical simulation. The methodis successfully applied to actual multi-temporal and multi-spectral images. 1. INTRODUCTION Change analysis is one of the most important processings for monitoring environment. It provides us with information aboutchange, for example, of natural environment to urban area. In change analysis, it is required to analyze the particular changerather than to detect all types of change. There have been a lot of methods reported for change detection[1]. Most of them,however, detect all types of change without specifying the change[2], or analyze the changes categorically using supervisedclassification of two multi-temporal images[3][4]. In the former, the change to be analyzed is detected indirectly using cluster-ing, and in the latter, classification error is serious. A direct method has been required.We propose a method PACE (Particular Change Extractor) for analyzing temporal change with weight of significance. Themethod PACE enables us to select a particular type of change such as from vegetation to bare soil, and extracts areas in whichspectral pattern has the particular change between two multi-temporal and multi-spectral images. The method consists of threeprocedures; data projection, change detection and suppression of irrelevant changes. In the first two, we produce a feature spacederived from an assigned change so that the change is emphasized. Canonical correlation analysis[5] is used for it. We projecta set of image data onto the feature space. The difference between two transformed image data indicates temporal changes. As