Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
Abstract
Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers.
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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 [13]
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- Authors
- Fang, L., Li, S., Kang, X., & Benediktsson, J. A.
- Affiliations
- Hunan University (CN)
- University of Iceland (IS)
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2014-12
- Volume
- 52
- Issue
- 12
- Pages
- 7738-7749
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2052160904
- Cited by
- 321
- References
- 52
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 17 · 2023: 11 · 2022: 26 · 2021: 34 · 2020: 37 · 2019: 51 · 2018: 40 · 2017: 44 · 2016: 28 · 2015: 18 · 2014: 3
- FWCI
- 47.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61172161)
- National Natural Science Foundation for Distinguished Young Scholars of China (61325007)
- Fundamental Research Funds for the Central Universities, Hunan University
- Scholarship Award for Excellent Doctoral Student granted by the Chinese Ministry of Education
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- hyperspectral image classification, sparse representation, spatial context, spectral-spatial features
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