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Proceedings Paper

Use of machine vision techniques to detect human settlements in satellite images
Author(s): Chandrika Kamath; Sailes K. Sengupta; Douglas N. Poland; John A. H. Futterman
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Paper Abstract

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.

Paper Details

Date Published: 28 May 2003
PDF: 11 pages
Proc. SPIE 5014, Image Processing: Algorithms and Systems II, (28 May 2003); doi: 10.1117/12.477745
Show Author Affiliations
Chandrika Kamath, Lawrence Livermore National Lab. (United States)
Sailes K. Sengupta, Lawrence Livermore National Lab. (United States)
Douglas N. Poland, Lawrence Livermore National Lab. (United States)
John A. H. Futterman, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 5014:
Image Processing: Algorithms and Systems II
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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