Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation
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Published:2023-06-21
Issue:13
Volume:15
Page:3225
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Bu Lijing1, Dai Dong1, Zhang Zhengpeng1, Yang Yin23, Deng Mingjun1
Affiliation:
1. School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China 2. School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China 3. National Center for Applied Mathematics in Hunan Laboratory, Xiangtan 411105, China
Abstract
Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.
Funder
the National Key R&D Program of China Science and Technology Project of Hunan Provincial Natural Resources Department Scientific Research Project of Natural Resources in Hunan Province Postgraduate Scientific Research Innovation Project of Hunan Province Xiang-tan University
Subject
General Earth and Planetary Sciences
Reference48 articles.
1. Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh;Jalal;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2019 2. Zhang, P., Wang, N., Zheng, Z., Xia, J., Zhang, L., Zhang, X., Zhu, M., He, Y., Jiang, L., and Zhou, G. (2018, January 22–27). Monitoring of Drought Change in the Middle Reach of Yangtze River. Proceedings of the IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain. 3. Goetzke, R., Braun, M., Thamm, H.P., and Menz, G. (2008, January 7–11). Monitoring and modeling urban land-use change with multitemporal satellitedata. Proceedings of the IGARSS 2008—2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA. 4. Darweesh, M., Mansoori, S.A., and Alahmad, H. (2019, January 5–7). Simple Roads Extraction Algorithm Based on Edge Detection Using Satellite Images. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing, ICIVC, Xiamen, China. 5. Kussul, N., Shelestov, A., Yailymova, H., Yailymov, B., Lavreniuk, M., and Ilyashenko, M. (October, January 26). Satellite Agricultural Monitoring in Ukraineat Country Level: World Bank Project. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.
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