小组新闻

近日，小组成员徐建功的论文被人工智能领域顶级期刊 Information Fusion（一区TOP，IF=14.8）录用。这也是一年多来课题组研究成果再次在该顶级期刊上发表。

Abstract-Reconstruction of missing information in cloud-contaminated optical satellite images is an urgent problem to ensure the continuous spatiotemporal monitoring of Earth’s environment. Despite the recent notable advancements of polarimetric synthetic aperture radar (PolSAR)-fused methods in mitigating the impact of clouds in optical satellite images, challenges remain in recovering high-fidelity cloud-free imagery, which primarily stem from the domain gaps in multimodal imaging mechanisms, the inherent speckle noise in PolSAR imagery, and the inadequate utilization of PolSAR's physical scattering properties, particularly its polarization information. To address these, this paper proposes a novel spatial-channel collaborative interaction network (PolNet-CR), designed to efficiently remove clouds from optical satellite images by incorporating PolSAR data through cross-scale spatial fine-grained aggregation and sparse channel statistical majorization. The network's architecture consists of multiple sequentially stacked information incremental collaboration (IIC) blocks, forming a deep iterative hierarchical framework for more informative cross-domain feature adaptive extraction, interaction, and fusion. Additionally, this paper developed LuojiaSET-PolCR, the first public dataset explicitly incorporating PolSAR polarimetric scattering characteristics for cloud removal in optical imagery, to advance research in this field. Based on this dataset, this paper conducted comparative analyses of PolNet-CR against current representative cloud removal algorithms, employing the “4S” multi-dimensional integrated evaluation system, which assesses spatial, spectral, semantic, and scalability performance. Experimental results demonstrate that PolNet-CR achieves significant improvements in both quantitative metrics and qualitative visual perception, while also meeting the demand for spatiotemporal cloud-free imagery in seamless continuous observation applications. The project is publicly available at: https://github.com/RSIIPAC/PolNet-CR.