Abstract-The high number of spectral bands acquired by hyperspectral sensors increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. This letter presents a novel method for accurate spectral-spatial classification of hyperspectral images. The proposed technique consists of two steps. In the first step, a probabilistic support vector machine pixelwise classification of the hyperspectral image is applied. In the second step, spatial contextual information is used for refining the classification results obtained in the first step. This is achieved by means of a Markov random field regularization. Experimental results are presented for three hyperspectral airborne images and compared with those obtained by recently proposed advanced spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches. Index Terms-Classification, hyperspectral images, Markov random field (MRF), support vector machine (SVM).
In addition, in REF , a Markov random field regularization is applied to the result of a pixel-wise classification of hyperspectral images using a probabilistic support vector machine.
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters
Mathematics Computer Science