Convolutional neural network for road extraction

Junping Li; Yazhou Ding; Fajie Feng; Baoyu Xiong; Weihong Cui

doi:10.1117/12.2295806

15 November 2017 Convolutional neural network for road extraction

Junping Li, Yazhou Ding, Fajie Feng, Baoyu Xiong, Weihong Cui

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106053R (2017) https://doi.org/10.1117/12.2295806
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

In this paper, the convolution neural network with large block input and small block output was used to extract road. To reflect the complex road characteristics in the study area, a deep convolution neural network VGG19 was conducted for road extraction. Based on the analysis of the characteristics of different sizes of input block, output block and the extraction effect, the votes of deep convolutional neural networks was used as the final road prediction. The study image was from GF-2 panchromatic and multi-spectral fusion in Yinchuan. The precision of road extraction was 91%. The experiments showed that model averaging can improve the accuracy to some extent. At the same time, this paper gave some advice about the choice of input block size and output block size.

Citation Download Citation

Junping Li, Yazhou Ding, Fajie Feng, Baoyu Xiong, and Weihong Cui "Convolutional neural network for road extraction", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053R (15 November 2017); https://doi.org/10.1117/12.2295806

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