Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation
Abstract
A multiple-feature-based adaptive sparse representation (MFASR) method is proposed for the classification of hyperspectral images (HSIs). The proposed method mainly includes the following steps. First, four different features are separately extracted from the original HSI and they reflect different kinds of spectral and spatial information. Second, for each pixel, a shape adaptive (SA) spatial region is extracted. Third, an adaptive sparse representation algorithm is introduced to obtain the sparse coefficients for the multiple-feature matrix set of pixels in each SA region. Finally, these obtained coefficients are jointly used to determine the class label of each test pixel. Experimental results demonstrated that the proposed MFASR method can outperform several well-known classifiers in terms of both qualitative and quantitative results.
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Feature extraction and feature selection are dedicated to learning temporal, spatial and spectral features in images and even intrinsic priors in the production line to narrow intraclass variation and expand interclass distance.
“Essentially, feature extraction and feature selection are dedicated to learning the temporal, spatial [13-15] and spectral features [16, 17] in images and even the intrinsic priors in the production line [18] to narrow the intraclass variation and expand the interclass distance.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [14]
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Gao, Q., Lim, S., & Jia, X. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2915809 · Cited by 30 · see citing works
A Divisive Hierarchical Clustering Approach to Hyperspectral Band Selection
Ji, H., Zuo, Z., & Han, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3184353 · Cited by 28 · see citing works
Sparse Reconstruction-Based Thermal Imaging for Defect Detection
Roy, D., Babu, P., & Tuli, S. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2889364 · Cited by 27 · see citing works
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UCSL: Toward Unsupervised Common Subspace Learning for Cross-Modal Image Classification
Yao, J. T., Hong, D., Wang, H., Liu, H., & Chanussot, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3282951 · Cited by 27 · see citing works
Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network
Tu, B., He, W., He, W. P., Ou, X., & Plaza, A. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3133009 · Cited by 26 · see citing works
Spectral Difference Guided Graph Attention Autoencoder for Hyperspectral Anomaly Detection
Li, K., Ling, Q., Wang, Y., Cai, Y., Qin, Y., Lin, Z., & An, W. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3222499 · Cited by 26 · see citing works
A sparse tensor-based classification method of hyperspectral image
Liu, F., & Wang, Q. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.107361 · Cited by 26 · see citing works
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Unsupervised Domain Adaptation With Dense-Based Compaction for Hyperspectral Imagery
Yu, C., Liu, C., Yu, H., Song, M., & Chang, C. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3128932 · Cited by 25 · see citing works
ESSINet: Efficient Spatial–Spectral Interaction Network for Hyperspectral Image Classification
Lv, Z., Dong, X., Peng, J., & Sun, W. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3162721 · Cited by 25 · see citing works
Low-rank tensor learning for classification of hyperspectral image with limited labeled samples
He, Z., Hu, J. H., & Wang, Y. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.11.007 · Cited by 24 · see citing works
Hyperspectral Image Classification Using Adaptive Weighted Quaternion Zernike Moments
Li, H., Huang, H., Ye, Z., & Li, H. (2022) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2022.3144954 · Cited by 24 · see citing works
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Self-citationA Semi-Supervised High-Level Feature Selection Framework for Road Centerline Extraction
Liu, R., Miao, Q., Zhang, Y., Gong, M., & Xu, P. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2931928 · Cited by 23 · see citing works
Multiview-Based Random Rotation Ensemble Pruning for Hyperspectral Image Classification
Zhang, Y., Cao, G., & Li, X. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3011777 · Cited by 23 · see citing works
Xing, C., Cong, Y., Duan, C., Wang, Z., & Wang, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3171324 · Cited by 23 · see citing works
Spatial-spectral classification of hyperspectral image via group tensor decomposition
Zhao, G., Tu, B., Fei, H., Li, N., & Yang, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.052 · Cited by 23 · see citing works
Hyperspectral Image Classification via Superpixel Correlation Coefficient Representation
Tu, B., Yang, X., Li, N., Ou, X., & He, W. P. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2866901 · Cited by 23 · see citing works
A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification
Cui, B., Cui, J., Lu, Y., Guo, N., & Gong, M. (2020) · Remote Sensing · doi:10.3390/rs12040664 · Cited by 23 · see citing works
Haidarh, M., Mu, C., Liu, Y., & He, X. (2025) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2025.04.002 · Cited by 22 · see citing works
Wang, B., Ren, M., Xia, C., Li, Q., Dong, M., Zhang, C., Guo, C., Liu, W., & Pischler, O. (2022) · Measurement · doi:10.1016/j.measurement.2022.112058 · Cited by 21 · see citing works
Global-to-Local Spatial–Spectral Awareness Transformer Network for Hyperspectral Anomaly Detection
He, X., Zhou, S., Ling, Q., Li, M., Li, Z., Zhang, Y., & Lin, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3456799 · Cited by 21 · see citing works
Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification
Ma, Y., Zhang, Y., Mei, X., Dai, X., & Ma, J. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2949621 · Cited by 21 · see citing works
Multikernel Adaptive Collaborative Representation for Hyperspectral Image Classification
Du, P., Gan, L., Xia, J., & Wang, D. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2833882 · Cited by 20 · see citing works
Tu, B., Zhou, C., Kuang, W., Chen, S., & Plaza, A. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3176286 · Cited by 20 · see citing works
Feature Fusion With Predictive Weighting for Spectral Image Classification and Segmentation
Zhang, Y., Huynh, C. P., & Ngan, K. N. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2908679 · Cited by 19 · see citing works
Game Theory-Based Hyperspectral Anomaly Detection
Huang, Z., Kang, X., Shutao Li, A., & Hao, Q. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2958359 · Cited by 18 · see citing works
An Attention-Based Lattice Network for Hyperspectral Image Classification
Nikzad, M., Gao, Y., & Zhou, J. W. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3164341 · Cited by 18 · see citing works
Multiscale Spatial–Frequency-Domain Cross-Transformer for Hyperspectral Image Classification
Shi, C., Chen, P., Fang, L., Zhao, M., Hei, X., & Miao, Q. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3578703 · Cited by 17 · see citing works
Self-citationThe correlation-based tucker decomposition for hyperspectral image compression
Li, R., Pan, Z., Wang, Y., & Wang, P. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.073 · Cited by 17 · see citing works
Pooled hybrid-spectral for hyperspectral image classification
Banerjee, A., & Banik, D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13721-2 · Cited by 17 · see citing works
Fully Convolutional Network-Based Nonlocal-Dependent Learning for Hyperspectral Image Classification
Tu, B., He, W., Li, Q., Peng, Y., & Chen, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205669 · Cited by 17 · see citing works
Yao, G., Cui, J. G., Deng, K., & Zhang, L. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2751567 · Cited by 17 · see citing works
Shen, Y., Xiao, L., Chen, J., & Pan, D. (2019) · IEEE Access · doi:10.1109/access.2019.2940697 · Cited by 17 · see citing works
A Label Similarity Probability Filter for Hyperspectral Image Postclassification
Sun, W., Yang, G., Ren, K., Peng, J., Ge, C., Meng, X., & Du, Q. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3094197 · Cited by 17 · see citing works
Diagonalized Low-Rank Learning for Hyperspectral Image Classification
Xing, C., Wang, M., Wang, Z., Duan, C., & Liu, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3085672 · Cited by 16 · see citing works
Mutually exclusive-KSVD: Learning a discriminative dictionary for hyperspectral image classification
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Li, D., Kong, F., Liu, J., & Wang, Q. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3056722 · Cited by 15 · see citing works
Non-overlapping classification of hyperspectral imagery with superpixel segmentation
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Sparse-Representation-Based Image Reconstruction for Magnetic Particle Imaging
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Weighted Sparse Graph Regularization for Spectral–Spatial Classification of Hyperspectral Images
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Multiscale Adaptive Convolution for Hyperspectral Image Classification
Ren, Q., Tu, B., Li, Q., He, W., & Peng, Y. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3185125 · Cited by 13 · see citing works
Xing, C., Duan, C., Wang, Z., & Wang, M. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109123 · Cited by 12 · see citing works
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- Authors
- Fang, L., Wang, C., Li, S., & Benediktsson, J. A.
- Affiliations
- Hunan University (CN)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2017-07
- Volume
- 66
- Issue
- 7
- Pages
- 1646-1657
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2595902385
- Cited by
- 172
- References
- 49
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 10 · 2023: 15 · 2022: 25 · 2021: 27 · 2020: 21 · 2019: 27 · 2018: 24 · 2017: 10
- FWCI
- 18.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Funders
- National Natural Science Fund of China for International (Regional) Cooperation and Exchanges (61520106001)
- National Natural Science Foundation for Distinguished Young Scholars of China (61325007)
- National Natural Science Foundation for Young Scientists of China (61501180)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- hyperspectral image classification, multiple features, spectral-spatial features, sparse coefficients, pixel classification
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