Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 17, Issue 8, Pages 1435-1439Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2945424
Keywords
Hyperspectral imaging; Image edge detection; Spatial resolution; Histograms; Training; Contrast limited adaptive histogram equalization (CLAHE); deep residual convolutional neural network (DRCNN); guided filter; hyperspectral pansharpening
Categories
Funding
- China Postdoctoral Science Foundation [2017M623124]
- China Postdoctoral Science Special Foundation [2018T111019]
- National Natural Science Foundation of China [61901343, 61671383, 61571345, 91538101, 61501346, 61502367]
- 111 Project [B08038]
- Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology [LSIT201924W]
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Recently, deep learning (DL) has gained impressive achievements in the field of remote sensing image fusion. However, most of the previous DL-based fusion methods are originally designed for multispectral pansharpening, which cannot be readily employed to hyperspectral pansharpening due to the much wider spectral range and lower spatial resolution of a hyperspectral image (HSI). In this letter, a novel framework based on deep residual learning is proposed for hyperspectral pansharpening. The proposed framework consists mainly of two parts. First, the initialized HSI with the enhanced spatial resolution is generated through contrast limited adaptive histogram equalization (CLAHE) and guided filter. Then, a deep residual convolutional neural network (DRCNN) is introduced to map the residuals between the initialized HSI and the reference HSI for further boosting the fusion accuracy. Experimental results demonstrate that the proposed framework can achieve superior performance compared with the existing state-of-the-art pansharpening methods, especially in terms of edge details enhancement.
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