A survey of methods incorporating spatial information in image classification and spectral unmixing |
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| Authors: | Le Wang Chen Shi Chunyuan Diao Wenjie Ji Dameng Yin |
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| Affiliation: | 1. College of Resources Environment and Tourism, Capital Normal University, Beijing, China;2. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China;3. Department of Geography, University at Buffalo, The State University of New York, Buffalo, NY, USAlewang@buffalo.edu;5. Department of Geography, University at Buffalo, The State University of New York, Buffalo, NY, USA |
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| Abstract: | Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered. |
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