Use of machine vision techniques to detect human settlements in satellite images

The automated production of maps of human settlement from recent satellite images is essential to studies of urbanization, population movement, and the like. The spectral and spatial resolution of such imagery is often high enough to successfully apply computer vision techniques. However, vast amounts of data have to be processed quickly. In this paper, we propose an approach that processes the data in several difierent stages. At each stage, using features appropriate to that stage, we identify the portion of the data likely to contain information relevant to the identiflcation of human settlements. This data is used as input to the next stage of processing. Since the size of the data has reduced, we can now use more complex features in this next stage. Thesefeaturescanbemorerepresentativeofhumansettlements,andalsomoretimeconsumingtoextractfrom the image data. Such a hierarchical approach enables us to process large amounts of data in a reasonable time, whilemaintainingtheaccuracyofhumansettlementidentiflcation. Weillustrateourmulti-stageapproachusing IKONOS 4-band and panchromatic images, and compare it with the straight-forward processing of the entire image.