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PolSAR image deep learning super-resolution model based on multiscale attention mechanism

  • role： First author第一作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

  • Email：linliupeng@whu.edu.cn
  • Introduction：林镠鹏，研究方向为影像质量改善、数据融合等。E-mail： linliupeng@whu.edu.cn
LIN Liupeng1,  
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

LI Jie2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

    Hubei Luojia Laboratory, Wuhan 430079, China

  • Email：shenhf@whu.edu.cn
  • Introduction：沈焕锋，研究方向为资源环境遥感、多源数据融合、地学智能等。E-mail： shenhf@whu.edu.cn
SHEN Huanfeng13*

résumé

Fully Polarimetric Synthetic Aperture Radar imagery (PolSAR) can provide rich polarimetric information; however, given the limitations of the system’s signal bandwidth and the physical size of the antenna, the spatial resolution of the SAR imaging system is restricted while acquiring multiple polarization information. To solve this problem, on the basis of the deep learning framework, this study proposes a multiscale attention-based PolSAR super-resolution network (MS-PSRN), which performs super-resolution reconstruction on the low-resolution full-polarimetric SAR images to generate the fully polarimetric SAR images with high spatial resolution. Under this super-resolution reconstruction framework, this study uses a multiscale architecture to fully extract the feature information of objects at different scales. On this basis, the spatial attention mechanism and the channel attention mechanism are introduced to recalibrate the feature maps, which are used to enhance the reconstruction performance of spatial details and improve the ability to maintain polarization information, respectively. Two attention mechanism embedding methods, i.e., joint and separated, are proposed to cope with the spatial size and quantity changes of the feature maps processed by the encoder and decoder. This study introduces a residual information distillation mechanism, extracts discriminative features through feature distillation, and compresses model parameters at the same time. In addition, the adaptive loss function is proposed to constrain the network training process and improve the model’s numerical fitting ability and edge information preservation ability. In this study, the proposed method is verified by two datasets, namely, the simulated data and the real data produced by RADARSAT-2 images. The experimental results of spatial information show that the proposed method is superior to the comparison algorithms in terms of visual results and quantitative indicators and has higher texture detail reconstruction accuracy and lower reconstruction error. The polarimetric information preservation test shows that the proposed method can effectively preserve the polarimetric information of PolSAR images while improving spatial resolution.

mots-clés

remote sensing;fully polarimetric synthetic aperture radar;super resolution;deep learning;multi-scale;attention mechanism

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