Song, W.; He, H.; Dai, J.; Jia, G.; Fan, J. SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images. Preprints2024, 2024030833. https://doi.org/10.20944/preprints202403.0833.v1
APA Style
Song, W., He, H., Dai, J., Jia, G., & Fan, J. (2024). SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images. Preprints. https://doi.org/10.20944/preprints202403.0833.v1
Chicago/Turabian Style
Song, W., Guohui Jia and Jiakai Fan. 2024 "SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images" Preprints. https://doi.org/10.20944/preprints202403.0833.v1
Abstract
Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
