Using image hierarchies to interpret LANDSAT data
Most automatic LANDSAT image interpretation systems have used traditional Pattern Recognition techniques. Usually each pixel is classified into one of a number of categories by examining its spectral signature, without regard to its spatial context. A survey of such techniques and of computational v...
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| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/2429/20294 |
| Summary: | Most automatic LANDSAT image interpretation systems have used traditional Pattern Recognition techniques. Usually each pixel is classified into one of a number of categories by examining its spectral signature, without regard to its spatial context. A survey of such techniques and of computational vision techniques from Artificial Intelligence leads to the design of a new system that allows the spatial structure of the image to control the interpretation. This classifier uses a pyramidal, hierarchical structure of images. A number of experiments with the implementation on LANDSAT images of forest cover show that one can achieve improvements over a conventional classifier in both accuracy (number of pixels correctly interpreted) and readability (number of regions in the interpreted image) without sacrificing efficiency. === Science, Faculty of === Computer Science, Department of === Graduate |
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