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Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel(More)
Deep-learning-based methods often suffer from insufficient training samples when they are directly used in the task of Synthetical Aperture Radar (SAR) images classification, which in turn leads to poor performance. To alleviate this problem, this paper presents a feature-fused approach, in which several statistical features of SAR images are extracted and(More)
Tme series polarimetric SAR image classification relies on learned understanding of how the set of pixels in an image relate by relative position and how the information of different dates in a time series change as time goes on. In this paper, we firstly integrate the incoherent information in the spatial scale and the coherent information in the temporal(More)
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