Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume -, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3189049
Keywords
Pansharpening; Feature extraction; Superresolution; Spatial resolution; Convolutional neural networks; Bayes methods; Principal component analysis; DenseNet; hyperspectral (HS) pansharpening; residual hyper-dense network (RHDN); spatio-spectral fusion
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Funding
- National Natural Science Foundation of China [62101414]
- Young Talent Fund of Xi'an Association for Science and Technology [095920221320]
- Natural Science Basic Research Plan in Shaanxi Province of China [2021JQ-194, 2021JQ-197]
- Fundamental Research Funds for the Central Universities [XJS210108, XJS210104]
- China Post-Doctoral Science Foundation [2021M702546, 2021M702548]
- Scientific and Technological Activities for Overseas Students of Shaanxi Province [2020-017]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515110856, CJT160102]
- Ten Thousand Talent Program
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This article proposes a residual hyper-dense network (RHDN) that extends the DenseNet to solve the spatio-spectral fusion problem. The RHDN method utilizes a two-branch network and cascade residual hyper-dense blocks (RHDBs) to extract and fuse features from PAN and HS images, achieving significant improvements over existing approaches.
Spatio-spectral fusion of panchromatic (PAN) and hyperspectral (HS) images is of great importance in improving spatial resolution of images acquired by many commercial HS sensors. DenseNets have recently achieved great success for image super-resolution because they facilitate gradient flow by concatenating all the feature outputs in a feedforward manner. In this article, we propose a residual hyper-dense network (RHDN) that extends the DenseNet to solve the spatio-spectral fusion problem. The overall structure of the proposed RHDN method is a two-branch network, which allows the network to capture the features of HS images within and outside the visible range separately. At each branch of the network, a two-stream strategy of feature extraction is designed to process PAN and HS images individually. A convolutional neural network (CNN) with cascade residual hyper-dense blocks (RHDBs), which allows direct connections between the pairs of layers within the same stream and those across different streams, is proposed to learn more complex combinations between the HS and PAN images. The residual learning is adopted to make the network efficient. Extensive benchmark evaluations well demonstrate that the proposed RHDN fusion method yields significant improvements over many widely accepted state-of-the-art approaches.
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