The automated production of maps of human settlement from recent satellite images is essential to detailed studies of urbanization, population movement, and the like. Commercial satellite imagery is becoming available with sufficient spectral and spatial resolution to apply computer vision techniques previously considered only for laboratory (high resolution, low noise) images. In this paper we attempt to extract human settlement from IKONOS 4-band and panchromatic images using spectral segmentation together with a form of generalized second-order statistics and detection of edges, corners, and other candidate human-made features in the imagery.
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 different stages. At each stage, using features appropriate to that stage, we identify the portion of the data likely to contain information relevant to the identification 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. These features can be more
representative of human settlements, and also more time consuming to
extract from the image data. Such a hierarchical approach enables us to process large amounts of data in a reasonable time, while maintaining the accuracy of human settlement identification. We illustrate our multi-stage approach using IKONOS 4-band and panchromatic images, and compare it with the straight-forward processing of the entire image.