文献类型：期刊文献

英文题名：Cascaded Local–Nonlocal Pansharpening with Adaptive Channel-Kernel Convolution and Multi-Scale Large-Kernel Attention

作者：Yin, Junru[1] Huang, Zhiheng[1] Chen, Qiqiang[1] Huang, Wei[1] Sun, Le[2] Wu, Qinggang[1] Hou, Ruixia[3]

第一作者：Yin, Junru

机构：[1] College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; [2] School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China; [3] Research Institute of Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China

年份：2026

卷号：18

期号：1

外文期刊名：Remote Sensing

收录：EI(收录号：20260319914279);Scopus(收录号：2-s2.0-105027331953)

语种：英文

外文关键词：Arts computing - Image texture

摘要：Highlights: What are the main findings? A cascaded local–nonlocal pansharpening network (CLNNet) was developed to progressively refine spatial details and spectral fidelity by stacking PLNF modules. Coupling adaptive channel-kernel convolution with multi-scale large-kernel attention within each PLNF improved texture preservation and spectral fidelity. What are the implications of the main findings? The developed CLNNet could enable the generation of HRMS images for downstream remote sensing applications (e.g., classification and detection). The progressive local–nonlocal fusion strategy offers strong potential for other multi-source image fusion tasks requiring both fine textures and long-range dependencies. Pansharpening plays a crucial role in remote sensing applications, as it enables the generation of high-spatial-resolution multispectral images that simultaneously preserve spatial and spectral information. However, most current methods struggle to preserve local textures and exploit spectral correlations across bands while modeling nonlocal information in source images. To address these issues, we propose a cascaded local–nonlocal pansharpening network (CLNNet) that progressively integrates local and nonlocal features through stacked Progressive Local–Nonlocal Fusion (PLNF) modules. This cascaded design allows CLNNet to gradually refine spatial–spectral information. Each PLNF module combines Adaptive Channel-Kernel Convolution (ACKC), which extracts local spatial features using channel-specific convolution kernels, and a Multi-Scale Large-Kernel Attention (MSLKA) module, which leverages multi-scale large-kernel convolutions with varying receptive fields to capture nonlocal information. The attention mechanism in MSLKA enhances spatial–spectral feature representation by integrating information across multiple dimensions. Extensive experiments on the GaoFen-2, QuickBird, and WorldView-3 datasets demonstrate that the proposed method outperforms state-of-the-art methods in quantitative metrics and visual quality. ? 2025 by the authors.