Object and feature-space fusion and information mining for change detection

Abstract

Utilizing boundaries of segmented objects from a later temporal image to constrain the segmentation of an earlier co- registered image enables information about the spectral, textural, and other characteristic attributes of image segmented objects within the two images to be mined for differences that would be indicative of specific types of land use and land cover change. Significant changes in homogeneity, hue, and vegetation indices among others provide strong cues about changes that may have occurred within segmented objects. Depending on the nature of the initial segmentation and the degree to which it was designed to extract class features of a desired size, shape, color, and texture, the method described enables highly targeted change detection to be conducted to explore desired types of land use and land cover change. For a collection of precision orthorectified QuickBird bi-temporal images, segmentation results for later images are utilized to constrain the segmentation of earlier images. Object attributes of the segmented images that provide a feature space for defining class memberships functions are employed to determine areas that were changed