@inproceedings{f4b22c8da94143d387d491794939b10d,
title = "DesNet: Deep residual networks for descalloping of ScanSAR images",
abstract = "Scalloping is one of the critical problems in ScanSAR images. It not only affects image visualization, but also influences the quantitative applications such as surface wind and wave retrievals in the ocean area. The existing method of descalloping needs artificial parameter setting and lacks generality in the image domain. A novel deep neural network based on residual learning for descalloping of ScanSAR images is proposed in this paper. The proposed method can eliminate scalloping patterns and has strong adaptive ability, which can handle inhomogeneous scalloping patterns and different scenarios. Experiments on GF-3 ScanSAR images verify the good performance of this method. The code for our models is available online.",
keywords = "Deep neural network, Residual learning, Scalloping patterns, ScanSAR, Synthetic aperture radar (SAR)",
author = "Shangliang Xu and Xiaolan Qiu and Changbo Wang and Lihua Zhong and Xinzhe Yuan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8519078",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8929--8932",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
address = "美国",
}