A Large-Size Remote Sensing Image Classification Network Considering Spatial Adjacency Information

High spatial resolution (HSR) image scene classification is currently a research hotspot, aiming to assign semantic labels based on visual information. However, many scene classification methods currently focus on studying images with small-size coverage, rendering them unsuitable for large-size image, thus limiting their applicability. To solve this problem, a dual-branch large-size image classification network (LSICNet) is proposed, to organize the input data pipeline into a nine-grid form for achieving scene classification for large-size HSR image. At first, a large-size remote sensing image classification (LSRSIC) dataset is constructed with full annotations, providing essential data support for scene-level classification. Secondly, the central image is input into the vision transformer (ViT) branch to enable the extraction of long-range dependency information. The masked autoencoders (MAE) branch is employed to learn the reconstruction of the masked central image through an encoder-decoder architecture, while the spatial contextual relationships between the masked regions and the visible neighboring image regions are explored, thereby effectively capturing the contextual features of the adjacent images. Finally, features from both branches are concatenated to achieve complementary fusion. Experiments on the proposed LSRSIC dataset demonstrate that the LSICNet obtains quite competitive classification results compared with some state-of-the-art methods.