Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 59, Issue 6, Pages 4957-4971Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3020162
Keywords
Feature extraction; Spatial resolution; Image reconstruction; Kernel; Frequency modulation; Remote sensing; Convolutional neural network; multi-scale dense connection; pan sharpening; remote sensing; residual learning
Categories
Funding
- National Key Research and Development Program of China [2017YFB1402103]
- Changjiang Scholars and Innovative Research Team in University [IRT_17R87]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JQ-294, 2019JQ-454]
- China Postdoctoral Science Foundation [2018M643718]
- Open Fund of Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University [IPIU2019002]
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The article introduces a pan-sharpening method called PSMD-Net, which utilizes multiple MDBs and a GDC to extract rich features. Experimental results demonstrate that PSMD-Net produces fusion images with higher resolution than state-of-the-art methods.
Pan sharpening is used to fuse a low-resolution multispectral (MS) image and a high-resolution panchromatic (PAN) image to obtain a high-resolution MS image. This article proposes PSMD-Net, an end-to-end pan-sharpening method based on a multi-scale dense network. A shallow feature extraction layer (SFEL) extracts the shallow features from the original images, and these are used as an input to a global dense feature fusion (GDFF) network to learn the global features for image reconstruction. A multiscale dense block (MDB) is designed to fully extract the spatial and spectral information from the shallow features in the GDFF network. In the proposed network, multiple MDBs are stacked to extract rich, multi-scale dense hierarchical features, and a global dense connection (GDC) is designed to allow direct connections from the state of the current MDB to all subsequent MDBs to extract more advanced features. The extracted hierarchical features are sent to the global feature fusion layer (GFFL) to adaptively learn the global features for image reconstruction. Finally, global residual learning (GRL) is adopted to force the network to pay more attention to the changing part of the image. We perform experiments on simulated and real data from WorldView-2 and WorldView-3 satellites. Visual and quantitative assessment results demonstrate that PSMD-Net yields higher-resolution fusion images than the state-of-the-art methods.
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