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• W4380997032 abstract "Remote sensing image (RSIs) segmentation is widely used in urban planning, natural disaster detection and many other fields. Compared with natural scene images, RSIs have higher resolution, complex imaging, and diverse object shapes and sizes, while semantic segmentation methods based on deep learning often require many data labels. In this paper, we propose a semi-supervised RSIs segmentation network with multi-scale deformable threshold feature extraction module and mixed attention (MDMANet). First, a pyramid ensemble structure is used, which incorporates deformable convolution and bole convolution, to extract features of objects with different shapes and sizes and reduce the influence of redundant features. Meanwhile, a mixed attention (MA) is proposed to aggregate long-range contextual relationships and fuse low-level features with high-level features. Second, an FCN-based full convolution discriminator task network is designed to help evaluate the feasibility of unlabeled image prediction results. We performed experimental validation on three datasets, and the results show that MDMANet segmentation provides more significant improvement in accuracy and better generalization than existing segmentation networks." @default.
• W4380997032 created "2023-06-17" @default.
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• W4380997032 date "2023-11-01" @default.
• W4380997032 modified "2023-09-27" @default.
• W4380997032 title "MDMASNet: A dual-task interactive semi-supervised remote sensing image segmentation method" @default.
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• W4380997032 doi "https://doi.org/10.1016/j.sigpro.2023.109152" @default.
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