The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery
Abstract
As a powerful and promising statistical signal modeling technique, sparse representation has been widely used in various image processing and analysis fields. For hyperspectral image classification, previous studies have shown the effectiveness of the sparsity-based classification methods. In this paper, we propose a nonlocal weighted joint sparse representation classification (NLW-JSRC) method to improve the hyperspectral image classification result. In the joint sparsity model (JSM), different weights are utilized for different neighboring pixels around the central test pixel. The weight of one specific neighboring pixel is determined by the structural similarity between the neighboring pixel and the central test pixel, which is referred to as a nonlocal weighting scheme. In this paper, the simultaneous orthogonal matching pursuit technique is used to solve the nonlocal weighted joint sparsity model (NLW-JSM). The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparsity-based algorithms and the classical support vector machine hyperspectral classifier.
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Cited by
Spectral-spatial classification of hyperspectral data based on deep belief network
Chen, Y., Zhao, X., & Jia, X. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2388577 · Cited by 1,259
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
Fang, L., Li, S., Kang, X., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2318058 · Cited by 321
Liang, H., & Li, Q. (2016) · Remote Sensing · doi:10.3390/rs8020099 · Cited by 238
Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation
Fang, L., Wang, C., Li, S., & Benediktsson, J. A. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2664480 · Cited by 172
Metadata
- Authors
- Zhang, H., Li, J., Huang, Y., & Zhang, L.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2014-06
- Volume
- 7
- Issue
- 6
- Pages
- 2056-2065
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2144348684
- Cited by
- 284
- References
- 25
- FWCI
- 53.09
- Type
- journal article
- Language
- en
- Open access
- closed
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