Effective Pan-Sharpening by Multiscale Invertible Neural Network and Heterogeneous Task Distilling
文献类型：期刊论文
| 作者 | Zhou, Man2,3; Huang, Jie2; Fu, Xueyang2; Zhao, Feng2; Hong, Danfeng1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60 |
| 关键词 | Heterogeneous knowledge-distilling invertible neural network pan-sharpening transformer |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2022.3199210 |
| 通讯作者 | Fu, Xueyang(xyfu@ustc.cdu.cn) |
| 英文摘要 | As recognized, the ground-truth multispectral (MS) images possess the complementary information (e.g., high-frequency components) of low-resolution (LR) MS images, which can be considered as privileged information to alleviate the spectral distortion and insufficient spatial texture enhancement. Since existing supervised pan-sharpening methods only utilize the ground-truth MS image to supervise the network training, its potential value has not been fully explored. To accomplish this, we propose a heterogeneous knowledge-distilling pan-sharpening framework that distills pan-sharpening by imitating the ground-truth reconstruction task in both the feature space and network output. In our work, the teacher network performs as a variational autoencoder to extract effective features of the ground-truth MS. The student network, acting as pan-sharpening, is trained by the assistance of the teacher network with the process-oriented feature imitation learning. Moreover, we design a customized information-lossless multiscale invertible neural module to effectively fuse LRMS and panchromatic (PAN) images, producing expected pan-sharpened results. To reduce the artifacts generated by the knowledge distillation process, a knowledge-driven refinement subnetwork is further devised according to the pan-sharpening imaging model. Extensive experimental results on different satellite datasets validate that the proposed network outperforms the state-of-the-art methods both visually and quantitatively. The source code will be released at https://github.com/manman1995/pansharpening . |
| WOS关键词 | IMAGE FUSION ; FILTER |
| 资助项目 | National Natural Science Foundation of China (NSFC)[61901433] ; USTC Research Funds of the Double First-Class Initiative[YD2100002003] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000856251200002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China (NSFC) ; USTC Research Funds of the Double First-Class Initiative |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/129147]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Fu, Xueyang |
| 作者单位 | 1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Man,Huang, Jie,Fu, Xueyang,et al. Effective Pan-Sharpening by Multiscale Invertible Neural Network and Heterogeneous Task Distilling[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60. |
| APA | Zhou, Man,Huang, Jie,Fu, Xueyang,Zhao, Feng,&Hong, Danfeng.(2022).Effective Pan-Sharpening by Multiscale Invertible Neural Network and Heterogeneous Task Distilling.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60. |
| MLA | Zhou, Man,et al."Effective Pan-Sharpening by Multiscale Invertible Neural Network and Heterogeneous Task Distilling".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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