Journal
IEEE ACCESS
Volume 7, Issue -, Pages 84876-84889Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2925283
Keywords
Deep learning; densely connected convolutional neural network; feature extraction; hyperspectral image classification; multi-scale filter bank
Categories
Funding
- National Key R&D Program of China [2018YFC0830300]
- National Natural Science Foundation of China [61671312]
- Sichuan Science and Technology Program [2018HH0070]
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In recent years, various deep learning-based methods have been applied in hyperspectral image (HSI) classification. Among them, spectral-spatial approaches have demonstrated their power to yield high accuracies. However, these methods tend to be computationally expensive. Specifically, two classic ways to develop spectral-spatial approaches both suffer from significant limitations in cost reduction: multi-channel networks need a large parameter scale, and 3-D filters are inherent of computational complexity. To establish a cost-effective architecture for both training cost and parameter scale, while maintaining the high accuracy of spectral-spatial techniques, an end-to-end spectral-spatial dual-channel dense network (SSDC-DenseNet) is proposed. To explore high-level features, the densely connected structure is introduced to enable deeper network. Furthermore, a 2-D deep dual channel network is applied to replace the expensive 3-D filters to reduce the model scale. The experiments were conducted on three popular datasets: the Indian Pines dataset, University of Pavia dataset, and Salinas dataset. The results demonstrate the competitive performance of the proposed SSDC-DenseNet with respect to classification performance and computational cost compared with other state-of-the-art DL-based methods while obtaining a remarkable reduction of computational cost.
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