Mengdao Xing

TL;DR: A new space-time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images and the dynamic steering vector concept is proposed.

TL;DR: Wang et al. as discussed by the authors combined the global contextual information perception of transformers and the local feature representation capabilities of convolutional neural networks (CNNs) to innovatively propose a visual transformer framework based on contextual joint representation learning, referred to as CRTransSar.

TL;DR: Experimental results indicate that the proposed fusion method can improve the overall accuracy by up to 5% compared to using the original Sentinel-2A and has the potential to improve the satellite-based land cover classification accuracy.

TL;DR: This letter proposes a new motion compensation approach for high-squint and high-resolution airborne spotlight synthetic aperture radar (SAR) integrated with polar format algorithm (PFA), and processing results of airborne SAR real data are presented to demonstrate the validity of the proposed algorithm.

TL;DR: Based on the excellent adaptability of deep neural networks (DNNs) and the structured modeling capabilities of probabilistic graphical models, the cascaded fully-convolutional network (CFCN) is proposed to improve the performance of water body detection in high-resolution SAR images.