A change detection method for remotely sensed multispectral and multitemporal images using 3-D segmentation

A new method is proposed for detection of the temporal changes using three-dimensional (3-D) segmentation. The method is a kind of clustering methods for temporal changes. In the method, multitemporal images form a image block in 3-D space; x-y plane and time axis. The image block is first divided into spatially uniform sub-blocks by applying binary division process. The division rule is based on the statistical t-test using Mahalanobis distance between spatial coefficient vectors of a local regression model fitted to neighboring sub-blocks to be divided. The divided sub-blocks are then merged into clusters using a clustering technique. The block-based processing, like the spatial segmentation technique, is very effective in reduction of apparent changes due to noise. Temporal change is detected as a boundary perpendicular to the time axis in the segmentation result. The proposed method is successfully applied to actual multitemporal and multispectral LANDSAT/TM images.