The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification
Abstract
Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional hand-crafted feature extraction, deep learning can automatically learn features with discriminative information. However, two issues exist in applying deep learning to HSIs. One issue is how to jointly extract spectral features and spatial features, and the other one is how to train the deep model when training samples are scarce. In this paper, a deep convolutional neural network with two-branch architecture is proposed to extract the joint spectral-spatial features from HSIs. The two branches of the proposed network are devoted to features from the spectral domain as well as the spatial domain. The learned spectral features and spatial features are then concatenated and fed to fully connected layers to extract the joint spectral-spatial features for classification. When the training samples are limited, we investigate the transfer learning to improve the performance. Low and mid-layers of the network are pretrained and transferred from other data sources; only top layers are trained with limited training samples extracted from the target scene. Experiments on Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer data demonstrate that the learned deep joint spectral-spatial features are discriminative, and competitive classification results can be achieved when compared with state-of-the-art methods. The experiments also reveal that the transferred features boost the classification performance.
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Cites
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Cited by
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
Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification
Sun, L., Zhao, G., Zheng, Y., & Wu, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3144158 · Cited by 778
Liu, T., & Abd-Elrahman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.006 · Cited by 103
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
Metadata
- Authors
- Yang, J., Zhao, Y.-Q., & Chan, J. C.-W.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2017-08
- Volume
- 55
- Issue
- 8
- Pages
- 4729-4742
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2614326984
- Cited by
- 484
- References
- 11
- FWCI
- 32.22
- Type
- journal article
- Language
- en
- Open access
- closed
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