Road segmentation model of remote sensing image based on improved DeeplabV3+
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.
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