4 accepted papers
Remote sensing pansharpening aims to reconstruct spatial-spectral properties during the fusion of panchromatic (PAN) images and low- resolution multi-spectral (LR-MS) images, finally generating the high-resolution multi-spectral (HR- MS) images. In the mainstream modeling strategies, i.e., CNN and T…
Pansharpening aims to fuse the panchromatic (PAN) and low-resolution multispectral (LR-MS) images, finally generating the high-resolution multispectral (HR-MS) images by reconstructing the spatial-spectral properties. Recently, VMamba-based methods built upon the visual state space (VSS) have shown…
Pansharpening aims to reconstruct a high-fidelity multispectral (HR-MS) image by fusing a multispectral (MS) image and a panchromatic (PAN) image. However, conventional pansharpening methods often struggle to address the modal gap between PAN and MS images. In this paper, we propose a novel cross-mo…
Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) m…