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Journal article
A high-performance spectral-spatial residual network for hyperspectral image classification with small training data
Remote Sensing, Vol.12(19), 3137
2020
Abstract
In this paper, we propose a high performance Two-Stream spectral-spatial Residual Network (TSRN) for hyperspectral image classification. The first spectral residual network (sRN) stream is used to extract spectral characteristics, and the second spatial residual network (saRN) stream is concurrently used to extract spatial features. The sRN uses 1D convolutional layers to fit the spectral data structure, while the saRN uses 2D convolutional layers to match the hyperspectral spatial data structure. Furthermore, each convolutional layer is preceded by a Batch Normalization (BN) layer that works as a regularizer to speed up the training process and to improve the accuracy. We conducted experiments on three well-known hyperspectral datasets, and we compare our results with five contemporary methods across various sizes of training samples. The experimental results show that the proposed architecture can be trained with small size datasets and outperforms the state-of-the-art methods in terms of the Overall Accuracy, Average Accuracy, Kappa Value, and training time.
Details
- Title
- A high-performance spectral-spatial residual network for hyperspectral image classification with small training data
- Authors/Creators
- W.N. Khotimah (Author/Creator) - Department of Physics, Mathematics and InformaticsM. Bennamoun (Author/Creator) - Computer Science and Software EngineeringF. Boussaid (Author/Creator) - Electrical, Electronic and Computer EngineeringF. Sohel (Author/Creator) - Murdoch UniversityD. Edwards (Author/Creator) - School of Biological Sciences
- Publication Details
- Remote Sensing, Vol.12(19), 3137
- Publisher
- MDPI
- Identifiers
- 991005543584607891
- Copyright
- © 2020 by the authors
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology; Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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