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Carlos Rivera

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Proceedings Article | 7 May 2007 Paper

Comparative study of state-of-the-art algorithms for hyperspectral image analysis

Carlos Rivera-Borrero, Shawn Hunt

Proceedings Volume 6565, 656517 (2007) https://doi.org/10.1117/12.719686

KEYWORDS: Image resolution, Spatial resolution, Image classification, Image filtering, Algorithm development, Image analysis, Principal component analysis, Hyperspectral imaging, Fuzzy logic, Image enhancement

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This work studies the end-to-end performance of hyperspectral classification and unmixing systems. Specifically, it compares widely used current state of the art algorithms with those developed at the University of Puerto Rico. These include algorithms for image enhancement, band subset selection, feature extraction, supervised and unsupervised classification, and constrained and unconstrained abundance estimation. The end to end performance for different combinations of algorithms is evaluated. The classification algorithms are compared in terms of percent correct classification. This method, however, cannot be applied to abundance estimation, as the binary evaluation used for supervised and unsupervised classification is not directly applicable to unmixing performance analysis. A procedure to evaluate unmixing performance is described in this paper and tested using coregistered data acquired by various sensors at different spatial resolutions. Performance results are generally specific to the image used. In an effort to try and generalize the results, a formal description of the complexity of the images used for the evaluations is required. Techniques for image complexity analysis currently available for automatic target recognizers are included and adapted to quantify the performance of the classifiers for different image classes.

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