Browse by author
Lookup NU author(s): Dr Jie Su
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
IEEEIn Earth observation activities, cloud severely affects the interpretation of the high-resolution imagery, generated by optical satellites. Therefore, removing clouds from optical imagery becomes a topic of interest in the remote sensing field. Currently, most methods use auxiliary Synthetic Aperture Radar (SAR) images to reconstruct optical images by merging SAR and optical images into a deep learning network. However, the speckle noise of the SAR image is not taken into the consideration during feature fusion processing, leading to the blurry edges in the reconstructed optical images. To get fine-grained optical images, we propose a novel cloud removal framework based on the edge fusion of SAR and optical images. Firstly, the edge feature of SAR images is extracted by the GRHED. As the prior knowledge, it can provide fine-grained edge information for subsequent reconstruction work. Then channels from three modal data are stacked to guide the reconstruction of optical images by exploiting their correlations and interactions. Furthermore, a structural similarity (SSIM) loss function is introduced to optimize the training network and improve the coherence of the image structure. Experimental results confirm its advantages on the SEN12MS-CR dataset.
Author(s): Wen Z, Suo J, Su J, Li B, Zhou Y
Publication type: Article
Publication status: Published
Journal: IEEE Geoscience and Remote Sensing Letters
Year: 2023
Volume: 20
Online publication date: 21/08/2023
Acceptance date: 17/08/2023
ISSN (print): 1545-598X
ISSN (electronic): 1558-0571
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/LGRS.2023.3307240
DOI: 10.1109/LGRS.2023.3307240
Altmetrics provided by Altmetric