The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Classification of Hyperspectral Images by Gabor Filtering Based Deep Network
Abstract
In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-level spatial structures of different orientations and scales. Then, the Gabor features and spectral features are simply stacked to form the fused features. Afterwards, deep features are captured by training a stacked sparse autoencoder deep network with the fused features obtained above as inputs. Since the number of training samples of hyperspectral images is often very limited, which negatively affects the classification performance in deep learning, an effective way of constructing virtual samples is designed to generate more training samples, automatically. By jointly utilizing both the real and virtual samples, the parameters of the deep network can be better trained and updated, which can result in classification results of higher accuracies. Experiments performed on four real hyperspectral datasets show that the proposed method outperforms several recently proposed classification methods in terms of classification accuracies.
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“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 [17]
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Deep 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
Deep learning classifiers for hyperspectral imaging: A review
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Rasti, B., Hong, D., Hang, R., Ghamisi, P., Kang, X., Chanussot, J., & Benediktsson, J. A. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.2979764 · Cited by 575
Self-citationLearning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification
Zhou, P., Han, J., Cheng, G., & Zhang, B. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2893180 · Cited by 290
Feature extraction for hyperspectral image classification: a review
Kumar, B., Dikshit, O., Gupta, A., & Singh, M. K. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1736732 · Cited by 218
Metadata
- Authors
- Kang, X., Li, C., Li, S., & Lin, H.
- Affiliations
- Hunan University (CN)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2018-04
- Volume
- 11
- Issue
- 4
- Pages
- 1166-1178
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2768309288
- Cited by
- 151
- References
- 47
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 10 · 2023: 15 · 2022: 24 · 2021: 34 · 2020: 27 · 2019: 22 · 2018: 14
- FWCI
- 14.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- Funders
- National Natural Science Fund of China for International Cooperation and Exchanges (61520106001)
- National Natural Science Foundation of China (61601179)
- National Natural Science Fund of China for Distinguished Young Scholars (61325007)
- Fund of Hunan Province for Science and Technology Plan Project (2017RS3024)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › 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, Gabor filters, Gabor features, spectral-spatial features, feature fusion, stacked sparse autoencoder, deep networks, synthetic training data, limited training data
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