Genetic algorithms for terrain categorization of Landsat images

David E. Larch

doi:10.1117/12.169475

25 February 1994 Genetic algorithms for terrain categorization of Landsat images

David E. Larch

Proceedings Volume 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs; (1994) https://doi.org/10.1117/12.169475
Event: 22nd Applied Imagery Pattern Recognition Workshop, 1993, Washington, DC, United States

Abstract

We have developed a method that uses genetic algorithms (GAs) to optimize rules for categorizing the terrain in Landsat data. A rule has two parts: a left side (the `if' clause) and a right side (the `then' clause). When the `if' clause is true, the functions in the `then' clause are executed to process the Landsat data. Examples of functions for processing the data include pixel by pixel threshold and a linear combination of six bands. Optimized rules are used to identify different terrain categories within Landsat data. Optimization is performed by comparing the results of the rules with ground truth using an objective function which minimizes the number of false positives and false negatives. Those rules that generate results close to the ground truth (those rules that return a small number of false positive and false negative pixel identifications) are highly rewarded and are used to create the next generation of rules. United States Geological Survey Land Use Land Cover data and an analyst's interpretation of the Landsat image were used as ground truth. The GA produced promising results for terrain categorization. More work in the area of terrain categorization is planned to build on these promising results.

Citation Download Citation

David E. Larch "Genetic algorithms for terrain categorization of Landsat images", Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); https://doi.org/10.1117/12.169475

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