27 February 2015 Classification of hyperspectral images based on conditional random fields
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Abstract
A significant increase in the availability of high resolution hyperspectral images has led to the need for developing pertinent techniques in image analysis, such as classification. Hyperspectral images that are correlated spatially and spectrally provide ample information across the bands to benefit this purpose. Conditional Random Fields (CRFs) are discriminative models that carry several advantages over conventional techniques: no requirement of the independence assumption for observations, flexibility in defining local and pairwise potentials, and an independence between the modules of feature selection and parameter leaning. In this paper we present a framework for classifying remotely sensed imagery based on CRFs. We apply a Support Vector Machine (SVM) classifier to raw remotely sensed imagery data in order to generate more meaningful feature potentials to the CRFs model. This approach produces promising results when tested with publicly available AVIRIS Indian Pine imagery.
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Yang Hu, Eli Saber, Sildomar Monteiro, Nathan D. Cahill, David W. Messinger, "Classification of hyperspectral images based on conditional random fields", Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 940510 (27 February 2015); doi: 10.1117/12.2083374; https://doi.org/10.1117/12.2083374
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