The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
Abstract
A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond to the weights of the selected training samples. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Two different approaches are proposed to incorporate the contextual information into the sparse recovery optimization problem in order to improve the classification performance. In the first approach, an explicit smoothing constraint is imposed on the problem formulation by forcing the vector Laplacian of the reconstructed image to become zero. In this approach, the reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. Experimental results show that our algorithm outperforms the classical supervised classifier support vector machines in most cases.
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Cites
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Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
Cited by
Hyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
Self-citationDeep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007
Self-citationGhamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
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
Zabalza, J., Ren, J., Zheng, J., Zhao, H., Qing, C., Yang, Z., Du, P., & Marshall, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.11.044 · Cited by 389
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
Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
Cheng, T., & Wang, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2936609 · Cited by 266
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
Manifold, B., Men, S., Hu, R., & Fu, D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00309-y · Cited by 152
Classification of Hyperspectral Images by Gabor Filtering Based Deep Network
Kang, X., Li, C., Li, S., & Lin, H. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2767185 · Cited by 151
Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers
Yin, J., Li, S., Zhu, H., & Luo, X. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2891076 · Cited by 58
Capsulenet-Based Spatial–Spectral Classifier for Hyperspectral Images
Arun, P. V., Buddhiraju, K. M., & Porwal, A. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2913097 · Cited by 37
Metadata
- Authors
- Chen, Y., Nasrabadi, N. M., & Tran, T. D.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2011-10
- Volume
- 49
- Issue
- 10
- Pages
- 3973-3985
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2097915756
- Cited by
- 1,155
- References
- 3
- FWCI
- 99.22
- Type
- journal article
- Language
- en
- Open access
- closed
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