Semantic segmentation of remote sensing images based on dual attention and multi-scale feature fusion

Mengqian Weng; Zhibo Hu; Xiaopeng Xie; Yunhong Li; Lei Hu

doi:10.1117/12.2589380

27 January 2021 Semantic segmentation of remote sensing images based on dual attention and multi-scale feature fusion

Mengqian Weng, Zhibo Hu, Xiaopeng Xie, Yunhong Li, Lei Hu

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200Z (2021) https://doi.org/10.1117/12.2589380
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

We propose a remote sensing image semantic segmentation model based on dual attention and multi-scale feature fusion to solve the problems of objects scale differences and missing small objects. This model uses ResNet50 in the coding part to extract features. First of all, the output features of each stage of ResNet50 are introduced into the pyramid pooling module, making full use of the multi-scale context information of the image to cope with the change of the object scales. Secondly, the dual attention is introduced in the final output features of ResNet50 to establish the semantic relationship between the spatial and channel dimensions, which enhances the ability of feature representation and improve the condition that small targets are difficult to segment. Finally, starting from the output features of the attention module, the features of all levels are gradually integrated to complete decoding to refine the target segmentation edge. The designed comparative experiments results show the effectiveness of the proposed method.

Citation Download Citation

Mengqian Weng, Zhibo Hu, Xiaopeng Xie, Yunhong Li, and Lei Hu "Semantic segmentation of remote sensing images based on dual attention and multi-scale feature fusion", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200Z (27 January 2021); https://doi.org/10.1117/12.2589380

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