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SVM and MRF-Based Method for Accurate Classification of Hyperspectral Images

IEEE geoscience and remote sensing letters, 2010-10, Vol.7 (4), p.736-740 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1545-598X ;EISSN: 1558-0571 ;DOI: 10.1109/LGRS.2010.2047711

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  • Title:
    SVM and MRF-Based Method for Accurate Classification of Hyperspectral Images
  • Author: Tarabalka, Yuliya ; Fauvel, Mathieu ; Chanussot, Jocelyn ; Benediktsson, Jon Atli
  • Subjects: Computer Science ; Image Processing
  • Is Part Of: IEEE geoscience and remote sensing letters, 2010-10, Vol.7 (4), p.736-740
  • Description: 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.
  • Publisher: IEEE - Institute of Electrical and Electronics Engineers
  • Language: English
  • Identifier: ISSN: 1545-598X
    EISSN: 1558-0571
    DOI: 10.1109/LGRS.2010.2047711
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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