Application of principal component analysis to multisensor classification

Brian D. Corner; Ram Mohan Narayanan; Stephen E. Reichenbach

doi:10.1117/12.339822

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29 January 1999 Application of principal component analysis to multisensor classification

Brian D. Corner, Ram Mohan Narayanan, Stephen E. Reichenbach

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Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339822
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States

Abstract

We are currently exploring the relationship between spatial statistical parameters of various geophysical phenomena and those of the remotely sensed image by way of principle component analysis (PCA) of radar and optical images. Issues being explored are the effects of incorporating PCA into land cover classification in an attempt to improve its accuracy. Preliminary results of using PCA in comparison with unsupervised land cover classification are presented.

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Brian D. Corner, Ram Mohan Narayanan, and Stephen E. Reichenbach "Application of principal component analysis to multisensor classification", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339822

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