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In the field of remote sensing imagery, road extraction is one of the key technologies supporting for Landuse Landcover classification. In this paper, a new semantic segmentation neural network named SAT U-Net is proposed for road extraction from remote sensing imagery. The new improved network replaces the sigmoid layer in the U-Net with a self-adaptive threshold method proposed to self-adaptively adjust the road thresholds for segmentation results of U-Net. The proposed method is combined with the strength of U-Net architecture to retain the complete road spatial features, thus overcomes the problem of unconnected and blurry roads in the segmentation results. To prove the effectiveness and utility of the proposed network, it was experimented on the test set of a public road dataset and compared with U-Net in five different environments. Experimental results demonstrate that the proposed method is superior to U-Net and presents clearer and more complete road structures. © 2019 IEEE.
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