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Hyperspectral Image Classification Using Relevance Vector Machines

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2 Author(s)
Demir, B. ; Kocaeli Univ., Kocaeli ; Erturk, S.

This letter presents a hyperspectral image classification method based on relevance vector machines (RVMs). Support vector machine (SVM)-based approaches have been recently proposed for hyperspectral image classification and have raised important interest. In this letter, it is genuinely proposed to use an RVM-based approach for the classification of hyperspectral images. It is shown that approximately the same classification accuracy is obtained using RVM-based classification, with a significantly smaller relevance vector rate and, therefore, much faster testing time, compared with SVM-based classification. This feature makes the RVM-based hyperspectral classification approach more suitable for applications that require low complexity and, possibly, real-time classification.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:4 ,  Issue: 4 )