Multiscale Road Extraction in Remote Sensing Images

Computational Intelligence and Neuroscience
Aziguli WulamuZheyu He

Abstract

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.

References

Jan 7, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Vijay BadrinarayananRoberto Cipolla
May 4, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Liang-Chieh ChenAlan L Yuille

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Methods Mentioned

BETA
feature extraction
feature extractor

Software Mentioned

DeepLabv3
net
Net + +
LinkNet
PSPNet
DeepLab
SegNet
AlexNet
- NET
SENet

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