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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Desnet: Deep Residual Networks for Descalloping of Scansar Images
摘要： 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.
关键词： synthetic aperture radar (SAR),deep neural network,scalloping patterns,ScanSAR,Residual learning
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Forest Early Warning System Using ALOS-2/PALSAR-2 Scansar Data (JJ-FAST)
摘要： JJ-FAST is an operational forest early warning system using PALSAR-2/ScanSAR mode data. Its deforestation detection algorithm has been updated since 2016 and the current is version 1 (as of January, 2018) and the algorithm version 2 is preparing to implement in March 2018. Overall user’s accuracy of version-1 products was estimated from several field experiments, and achieved as 83.3%. There are omission errors in the version 1 products because of its simple HV change detection approach.
关键词： Deforestation detection,ALOS-2/PALSAR-2,JJ-FAST,ScanSAR
更新于2025-09-23 15:22:29