A novel pansharpening model based on two parallel network architectures
发布时间：2025-01-07 点击次数：
DOI码：10.1080/01431161.2024.2382847
所属单位：School of Remote Sensing and Information Engineering, Wuhan University, China
发表刊物：INTERNATIONAL JOURNAL OF REMOTE SENSING
关键字：Deep Learning (DL),multi-level architecture,multi-scale architecture,pansharpening,remote sensing
摘要：Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance.
合写作者：Li, Lingli,Zhao, Jiansong, Pengcheng, Huan,Yang, Linze
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：45
期号：17
页面范围：5978-6003
ISSN号：0143-1161
是否译文：否
CN号：WOS:001287778500001,Scopus:2-s2.0-85201102608,EI:20243316883163
发表时间：2024-09-01
