Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 128, Issue -, Pages 223-239Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2017.03.020
Keywords
Remote sensing image classification; Spatial regularization; Markov Random Fields (MRFs); Spatial energy function; Spectral dissimilarity; Class co-occurrence dependency
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Funding
- National Natural Science Foundation of China [41571372, 91338111, 41301470]
- China National Science Fund for Excellent Young Scholars [41522110]
- Foundation for the Author of National Excellent Doctoral Dissertation of PR China [201348]
- Canada Research Chairs Program
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This paper develops a novel Markov Random Field (MRF) model for edge-preserving spatial regularization of classification maps. MRF methods based on the uniform smoothness lead to oversmoothed solutions. In contrast, MRF methods which take care of local spectral or gradient discontinuities, lead to unexpected object particles around boundaries. To solve these key problems, our developed MRF method first establishes a spatial energy function integrating local spectral dissimilarity to smooth the initial classification map while preserving object boundaries. Second, a new anisotropic spatial energy function integrating the class co-occurrence dependency is constructed to regularize pixels around object boundaries. The effectiveness of the method is tested using a series of remote sensing data sets. The obtained results indicate that the method can avoid oversmoothing and significantly improve the classification accuracy with regards to traditional MRF classification models and some other state-of-the-art methods. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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