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- W3207885783 abstract "Deep sub-pixel mapping network (DSMNet) is a state-of-the-art approach in the field of sub-pixel mapping (SPM, also called super resolution mapping), combining deep learning theory, to solve the mixed pixel problem, which is ubiquitous in remote sensing images due to the spatial-resolving limitation. However, traditional DSMNet usually do not consider the multi-scale distribution characteristics of the real geographical distribution exposed in urban landscape. Furthermore, the heterogeneous distribution characteristics (high-frequency components) are the most important for SPM, but are difficult to learn and usually ignored in the tradition network models. In this paper, the high-frequency component aware (HFCA) module was proposed, based on the hierarchical supervised deep sub-pixel mapping network (HiDSMNet). HiDSMNet establishes a hierarchical supervised architecture for explicit multi-scale supervision to prompt the network to learn a multi-scale representation. Besides, HFCA module is integrated to prompt the network to intensify the learning of the high-frequency representation. The experimental results with three public datasets validated the superiority of the proposed HiDSMNet." @default.
- W3207885783 created "2021-10-25" @default.
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- W3207885783 date "2021-07-11" @default.
- W3207885783 modified "2023-09-28" @default.
- W3207885783 title "Rethinking the High Frequency Components in Deep Sub-Pixel Mapping Network" @default.
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- W3207885783 doi "https://doi.org/10.1109/igarss47720.2021.9553075" @default.
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