Deep Fusion of Localized Spectral Features and Multi-scale Spatial Features for Effective Classification of Hyperspectral Images |
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| Institution: | 1. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China;2. Laboratory for Marine Resources Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China;3. School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT2600, Australia;4. School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou, 510665, China;5. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, United Kingdom;6. Satellite Environment Center (SEC), Ministry of Environmental Protection (MEP) of China, Beijing, 100094, China |
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| Abstract: | This study presents a deep extraction of localized spectral features and multi-scale spatial features convolution (LSMSC) framework for spectral-spatial fusion based classification of hyperspectral images (HSIs). First, adjacent spectral bands are grouped based on their similarity measurements, where the whole hypercube is partitioned into several sub-cubes, each corresponding to one band group. Then, the proposed localized spectral features extraction (LSF) strategy is used to extract localized spectral features, which are extracted from each band group using the 1D convolutional neural network (CNN). Meanwhile, the proposed HiASPP strategy is employed to extract the multi-scale features from the first several principal components of each sub-cube. Finally, the extracted spectral and spatial features are concatenated for spectral-spatial fusion based classification of HSI. Experiments conducted on three publicly available datasets have demonstrated that the proposed architecture outperforms several state-of-the-art approaches. |
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| Keywords: | features extraction hyperspectral image (HSI) classification convolutional neural network (CNN) deep learning |
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