期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 13, 期 -, 页码 1258-1270出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2020.2982614
关键词
Deep learning; densely connected convolutional neural network; hyperspectral image (HSI) classification; multilayer feature fusion
类别
资金
- National Natural Science Foundation of China [61922013, 61902339, 61703287]
- Liaoning Provincial Natural Science Foundation of China [20180550337, 2019-MS-254, 20180550664]
- Foundation of Liaoning Educational Committee [JYT19029]
- Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data in Yanan University [IPBED14]
- Doctoral Starting up Foundation of Yan'an University [YDBK2019-06]
Deep spectral-spatial features fusion has become a research focus in hyperspectral image (HSI) classification. However, how to extract more robust spectral-spatial features is still a challenging problem. In this article, a novel deep multilayer fusion dense network (MFDN) is proposed to improve the performance of HSI classification. The proposed MFDN simultaneously extracts the spatial and spectral features based on different sample input sizes, which can extract abundant spectral and spatial correlation information. First, the principal component analysis algorithm is performed on hyperspectral data to extract low-dimensional HSI data, and then the spatial features are extracted from the low-dimensional 3-D HSI data through 2-D convolutional, 2-D dense block, and average-pooling layers. Second, the spectral features are extracted directly from the raw 3-D HSI data by means of 3-D convolutional, 3-D dense block, and average-pooling layers. Third, the spatial and spectral features are fused together through 3-D convolutional, 3-D dense block, and average-pooling layers. Finally, the fused spectral-spatial features are sent into two full connection layers to extract high-level abstract features. Furthermore, densely connected structures can help alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and improve the HSI classification accuracy. The proposed fusion network outperforms the other state-of-the-art methods especially with a small number of labeled samples. Experimental results demonstrate that it can achieve outstanding hyperspectral classification performance.
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