MAFNet: A Multiangle Attention Fusion Network for Land Cover Classification - Guoying Miao; Huiqin Wang; Enwei Zhang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023)
The classification of land cover types is an important task for monitoring land use. Moreover, with the continuous application of high resolution remote sensing images, the time and space span is becoming larger, which greatly increases the difficulty of classification of target types. And there are few results that can effectively deal with deep land category information. Furthermore, the extraction and fusion of deep features still need to be improved. In this article, a multiangle attention fusion network is proposed for the classification of land cover types. The network uses a 50-layer residual network as a feature extraction network, and an adaptive special-shaped window attention module is added to the deep layer of the network to extract deep semantic information, building the connection between global information. In addition, the multiangle interactive attention fusion module is used to fuse feature maps at different levels, and an interactive attention mechanism is established at different angles. Finally, a new decoder module is proposed for the decoder to adjust the fused feature map again. Through experiments on three datasets, the results show that the method proposed in this article is more accurate than the previous network for the segmentation results of different categories. It can realize the accurate division of land types in remote sensing images, and has good generalization ability.