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