Multispectral Satellite Image Understanding

A problem of major interest to regional planning organizations, disaster relief agencies, and the military is the identification and tracking of land development across large scale regions, and over time. We develop an autonomous image analysis system to understand land development, especially residential and urban building organizations from satellite images. We introduce a set of measures based on straight lines to assess land development levels in high resolution satellite images. Urban areas exhibit a preponderance of straight line features. Rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent, more computationally intensive analyses. Vegetation indices have been used extensively to estimate the vegetation density from satellite and airborne images for many years. We use these as the multispectral information for classification and house and road extraction. We focus on the normalized difference vegetation index (NDVI) and introduce a statistical framework to analyze and extend it. Using the established statistical framework, we introduce new a group of shadow-water indices. We then extend our straight line based measures by developing a synergistic approach that combines structural and multispectral information. In particular, the structural features serve as cue regions for multispectral features. After the initial classification of regions, we introduce computationally more expensive but more precise graph theoretical measures over grayscale images to detect residential regions. The graphs are constructed using lines as vertices, while graph edges encode their spatial relationships. We introduce a set of measures based on various properties of the graph. These measures are monotonic with increasing structure (organization) in the image. We present a theoretical basis for the measures. Having detected the residential regions, we introduce a novel system to detect houses and street networks in these. We extensively use the multispectral information and graph theory to extract houses and road networks. We evaluated the performance of each step statistically and obtained very promising results. Especially, detection performances in house and street detection in residential regions is noteworthy. These results indicate the functionality of our satellite image understanding system.