NCGLF2: Network combining global and local features for fusion of multisource remote sensing data

• DOI: 10.1016/j.inffus.2023.102192
• Publication Date: 2023-12-15T20:40:52Z
• Authors: Bing Tu; Qi Ren; Jun Li; Zhaolou Cao; Yunyun Chen; Antonio Plaza

The fusion of multisource remote sensing (RS) data has demonstrated significant potential in target recognition and classification tasks. However, there is limited emphasis on capturing both high- low-frequency information from these sources. Additionally, effectively integrating remains a challenging task, as the absence redundancy discriminant hampers applications RS data. In this paper, we propose network called combining global local features (NCGLF2) that integrates (GLF) extracted This approach leverages capabilities convolutional neural networks (CNNs) to extract high frequency while utilizing transformer architecture replicate low correlations. Firstly, scale aggregation (SIA) module extracts multiscale shallow layer input Secondly, structural learning (SIL-Trans) captures features, an invertible (INN) learns information. Finally, GLF maximizes complementary characteristics fuse Our experimental results with three benchmark datasets indicate NCGLF2 outperforms existing state-of-the-art approaches terms feature representation compatibility diverse types. code available at https://github.com/renqi1998/NCGLF2.