期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 8, 页码 5799-5812出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2902431
关键词
Adversarial learning; dense connection; edge enhancement; remote sensing imagery; superresolution
类别
资金
- National Natural Science Foundation of China [61671332, U1736206, 61671336, 61501413, 61502354]
- National Key Research and Development Project [2016YFE0202300]
- Hubei Province Technological Innovation Major Project [2017AAA123]
- Central Government Guided Local Science and Technology Development Projects [2018ZYYD059]
- Basic Research Program of Shenzhen City [JCYJ20170306171431656]
The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e. g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the- art SR approaches.
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