1.College of Electronic and Information Engineering,Nanjing University of Information Science and Technology，2.School of Electronic Information Engineering,Wuxi University 在期刊界中查找 在百度中查找 在本站中查找
1.College of Electronic and Information Engineering,Nanjing University of Information Science and Technology，2.School of Electronic Information Engineering,Wuxi University 在期刊界中查找 在百度中查找 在本站中查找
In response to the problem of fuzzy boundaries and difficulty in distinguishing occlusions in road segmentation of remote sensing images,this paper proposes a remote sensing image road segmentation model based on an improved DeeplabV3+.The model introduces MobileNetV3 and ECA attention mechanism in the backbone network to reduce parameter volume and focus on continuous road feature information.In the decoding process,multi-level upsampling is adopted to enhance the tight connection between the encoder and decoder,fully preserving detailed information. Meanwhile,deep separable dilated convolution(DS-ASPP)is used in the ASPP module to significantly reduce the number of parameters.The experimental results demonstrate that the model achieves an intersection over union(IoU)of 83.71%and an accuracy of 93.71%on the Massachusetts Roads dataset.With a parameter count of 55.57×10⁶,the model exhibits superior segmentation accuracy and effectively avoids errors and omissions caused by boundary blurring and occlusion.It enhances both precision and speed in remote sensing road segmentation.
