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The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a selectable Transformer and Gist CNN network (STGC-Net). It designs a subspace similar recombination module (SSR-Module) based on non-negative matrix factorization (NMF) and the self-attention mechanism for feature decomposition. This can alleviate the redundant information of multi-modal data and extract their own singular and common features. Considering that the MS and the PAN images exhibit different advantageous properties, a selectable self-attention spectral feature extraction module (S 3 FE-Module) and a multi-stream Gist spatial feature extraction module (MGSFE-Module) are proposed for the different singular features. The former can refine the Transformer's input and simultaneously characterize the sequence information between channels for the MS image. The latter introduces the positional relationship between local features while extracting spatial features for the PAN image, thereby improving the accuracy of scene classification. Experimental results indicate that the proposed method performs better than the other methods. The relevant code of this paper is provided at: https://github.com/ru-willow/ST-GC-Net. • A subspace similar recombination module is proposed. • The selectable self-attention spectral feature extraction module is designed. • The multi-stream Gist spatial feature extraction module is introduced. [ABSTRACT FROM AUTHOR]

• A dual branch attention fusion deep network for classification is proposed. • An adaptive center offset sampling strategy for each patch is proposed. • Two attention based modules to highlight the advantages of two different data. • The feature level fusion is achieved by the proposed network. In recent years, with the diversification of acquisition methods of very high resolution panchromatic (PAN) and multispectral (MS) remote sensing images, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of data–driven deep learning, we design a dual–branch attention fusion deep network (DBAF–Net) for the multiresolution classification. It aims to integrate the feature–level fusion and classification into an end–to–end network model. In the process of establishing a training sample library, unlike the traditional pixel–centric sampling strategy with fixed patch size, we propose an adaptive center–offset sampling strategy (ACO–SS), which allows each patch to adaptively determine the neighborhood range by finding the texture structure of the pixel to be classified. And the neighborhood range is not symmetrical with this pixel, we expect to capture the neighborhood information that is more conducive to its classification. In network structure, based on the captured patches by ACO–SS, we design a spatial attention module (SA–module) for PAN data and a channel attention module (CA–module) for MS data, thus highlighting the spatial resolution advantages of PAN data and the multi–channel advantages of MS data, respectively. Then these two features are interfused to improve and strengthen the fusion features in both spatial and channel. The quantitative and qualitative experimental results verify the robustness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]