A position-aware attention network with progressive detailing for land use semantic segmentation of Remote Sensing images

ABSTRACT

Deep learning has achieved remarkable success in the semantic segmentation of remote sensing images (RSIs).In the domain of semantic segmentation, where classification and localization tasks need to be performed simultaneously, it is crucial to consider both global and local spatial relationships in RSIs. This is especially important for the recognition of ground objects that have a slim and elongated appearance. However, existing methods for land use semantic segmentation lack an effective mechanism to coordinate and address these two aspects, resulting in limitations on the recognition of slim targets and the continuity of land object identification. Here, a specific attention-based network called PaANet is developed for semantic segmentation. Our proposed framework builds upon the Swin transformer by incorporating two key modules: the position-aware attention (PaA) module and the pyramid pooling expectation-maximization (PPEM) module. These modules provide significant improvements in recognition accuracy and the continuity of ground object recognition while preserving structural classification details. Furthermore, we propose a multiresolution data augmentation method that utilizes scale-related information to guide the encoder. This approach leads to improved performance and generalization ability for the model. In experiments, the mIoU of our approach for the BLU and GID datasets is 2.37% and 3.94% higher than that of the baseline model, respectively. Our results also show significant superior to those of other methods regarding the continuity of ground object recognition.

KEYWORDS:

• Semantic Segmentation
• Remote Sensing Images
• Attention mechanism
• Land-use Classification

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The BLU dataset and GID dataset in this study are downloaded at https://rslab.disi.unitn.it/dataset/BLU/and https://x-ytong.github.io/project/GID.html

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant number 41971365, 32371966), the Major Science and Technology Project of the Ministry of Water Resources (Grant number SKR-2022037), the Chongqing Higher Education Teaching Reform Research Project (Grant number 201019S), and the Chongqing Graduate Research Innovation Project (Grant number CYS22448).