ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-5-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 101–107, 2020
https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 101–107, 2020
https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020

  03 Aug 2020

03 Aug 2020

MULTI-TEMPORAL SAR IMAGE DESPECKLING BASED A CONVOLUTIONAL NEURAL NETWORK

C. Zhou1, J. Li2, H. Shen1,3, and Q. Yuan2,3 C. Zhou et al.
  • 1School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
  • 2School of Geodesy and Geomatics, Wuhan University, Wuhan, China
  • 3Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China

Keywords: Multi-temporal SAR Despeckling, Speckle, Convolutional Neural Network, Spatio-temporal Information

Abstract. Speckle noise is an intrinsic property of Synthetic Aperture Radar (SAR) imagery, which affects the quality of image. Single-temporal despeckling methods usually pay attention to the utilization of spatial information, but sometimes due to lack of sufficient information, the despeckling image is too smooth or losses some information about edge details. However, multi-temporal SAR images can provide extra information for despeckling resulting in better performance. Therefore, in this paper, we proposed a novel multi-temporal SAR despeckling method based a convolutional neural network (MSAR-CNN) embedded temporal and spatial attention (TSA) module to deeply mine the spatial and temporal correlation of multitemporal SAR images. The whole network, which is end-to-end trained with simulate realistic SAR data, consists of several residual blocks. In addition, the simulated and real-data experiments demonstrate that the proposed MSAR-CNN outperforms most of the mainstream methods in both the quantitative evaluation indexes and visual effects.