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Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery
Abstract
An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. The main feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.
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Cited by
He, L., Li, J., Liu, C., & Li, S. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2765364 · Cited by 630
Self-citationLi, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014) · European Journal of Remote Sensing · doi:10.5721/eujrs20144723 · Cited by 538
Self-citationConvolutional Neural Networks for Multimodal Remote Sensing Data Classification
Wu, X., Hong, D., & Chanussot, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3124913 · Cited by 412
Metadata
- Authors
- Zhang, B., Li, S., Jia, X., Gao, L., & Peng, M.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2011-09
- Volume
- 8
- Issue
- 5
- Pages
- 973-977
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2113513024
- Cited by
- 194
- References
- 0
- FWCI
- 24.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery. https://app.cochat.ai/papers/10.1109/lgrs.2011.2145353
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- Zhang, B., Li, S., Jia, X., Gao, L., & Peng, M. (2011). Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2011.2145353
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