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A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation by Ma, Xiaowen; Lian, Rongrong; Wu, Zhenkai; Guan, Renxiang; Hong, Tingfeng; Zhao, Mengjiao; Ma, Mengting; Nie, Jiangtao; Du, Zhenhong; Song, Siyang; Zhang, Wei is a scholarly article available to read on EtoBox.

What is A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation about?

As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-range dependency modeling capability. However, remote sensing images are usually characterized by complex backgrounds and large intra-class variance that would degrade their analysis performance. While vanilla spatial attention mechanisms are based on dense affine operations, they tend to introduce a large amount of background contextual information and lack of consideration for intrinsic spatial correlation. To deal with such limitations, this paper proposes a novel scene-Coupling semantic mask network, which reconstructs the vanilla attention with scene coupling and local global semantic masks strategies. Specifically, scene coupling module decomposes scene information into global representations and object distributions, which are then embedded in the attention affinity processes. This Strategy effectively utilizes the intrinsic spatial correlation between features so that improve the process of attention modeling. Meanwhile, local global semantic masks module indirectly correlate pixels with the global

Author

Ma, Xiaowen; Lian, Rongrong; Wu, Zhenkai; Guan, Renxiang; Hong, Tingfeng; Zhao, Mengjiao; Ma, Mengting; Nie, Jiangtao; Du, Zhenhong; Song, Siyang; Zhang, Wei

Published

2025

Language

EN