Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines
Abstract
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral-spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral-spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral-spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral-spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral-spatial classification methods are applied on real-world hyperspectral data in our experiments.
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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 [16]
Features in the spatial-frequency domain show stronger robustness to noise and intensity variation than in the spatial domain, and transform features are useful in improving the representation of spectral data and increasing classification accuracy.
“Many works suggest that the features in the spatial-frequency domain show stronger robust to noise and intensity variation than in the spatial domain, and transform features have been reported to be useful in improving the representation of spectral data and increasing the classification accuracy [16].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [16]
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- Authors
- He, L., Li, J., Liu, C., & Li, S.
- Affiliations
- South China University of Technology (CN)
- Sun Yat-sen University (CN)
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2018-03
- Volume
- 56
- Issue
- 3
- Pages
- 1579-1597
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2767805377
- Cited by
- 630
- References
- 154
- Citations by year
- 2026: 35 · 2025: 67 · 2024: 77 · 2023: 78 · 2022: 86 · 2021: 96 · 2020: 76 · 2019: 85 · 2018: 29 · 2017: 1
- FWCI
- 43.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
- Funders
- National Natural Science Foundations of China (61771496, 61571195)
- Guangdong Provincial Natural Science Foundation (2016A030313254, 2016A030313516, 2017A030313382)
- 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, spectral-spatial classification, neighborhood information, label dependencies, hyperspectral imaging
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