18 October 2005 Hyperspectral classification applied to the Belgian coastline
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Abstract
Hyperspectral image classification impose challenging requirements to a classifier. It is well known that more spectral bands can be difficult to process and introduce problems such as the Hughes phenomenon. Nevertheless, user requirements are very demanding, as expectations grow with the available number of spectral bands: subtle differences in a large number of classes must be distinguished. As multiclass classifiers become rather complex for a large number of classes, a combination of binary classification results are often used to come to a class decision. In this approach, the posterior probability is retained for each of the binary classifiers. From these, a combined posterior probability for the multiclass case is obtained. The proposed technique is applied to map the highly diverse Belgian coastline. In total, 17 vegetation types are defined. Additionally, bare soil, shadow, water and urban area are also classified. The posterior probabilities are used for unmixing. This is demonstrated for 4 classes: bare soil and 3 vegetation classes. Results are very promosing, outperforming other approaches such as linear unmixing.
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Pieter Kempeneers, Pieter Kempeneers, Steve De Backer, Steve De Backer, Sam Provoost, Sam Provoost, Walter Debruyn, Walter Debruyn, Paul Scheunders, Paul Scheunders, } "Hyperspectral classification applied to the Belgian coastline", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820F (18 October 2005); doi: 10.1117/12.627602; https://doi.org/10.1117/12.627602
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