# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
###### Abstract  
Deep learning has been applied in various fields for its effective and accurate feature learning capabilities in recent years. Currently, information extracted from remote sensing images with the learning methods has become the most relevant research area for its developed precision. In terms of developing segmentation precision and reducing calculation power consumption, the improved deep learning methods have received more attention, and the improvement of semantic segmentation architectures has been a popular solution. This research presents a learning method named D-DenseNet with a new structure for road extraction. The methods for the improvement are divided into two stages: (1) alternate the consecutive dilated convolutions number in the structure of the network (2) the stem block is arranged as the initial block. So, dilated convolution can obtain more global context information through the whole network. Further, the D-DenseNet structures D-LinkNet by taking DenseNet as its backbone instead of ResNet, which can expand the receptive field and accept more feature information. The D-DenseNet is effective because of its 119 M model size and 57.96% IoU on the processing test data and 99.3 M modes size and 66.26% on the public dataset, which achieved the research objective for reducing model size and developing segmentation precision--IoU. The experiment indicates that the D-Dense block and the stem block are effective for developing road extraction, and the appropriate number of convolution layers is also essential for model evaluation. s +
Footnote †: journal: Medical Imaging  
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Footnote 1: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China 12 Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610225, China 2  
Footnote 1: footnotetext: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China 12 Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610225, China 12 Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610054, China 3  
Footnote 1: School of Software Engineering, Chengdu 610225, China 12 Sichuan Province Engineering Technology Research Center of Support Software of Software of Informatization Application, Chengdu 6100225, China 12 Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610054, China 3  
Footnote 1: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China 12 Sichuan Province Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610225, China 12 Sichuan Province Province Engineering Technology Research Center of Support Software of Software of Informatization Application, Chengdu 610225, China 12 Sichuan Province Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610054, China 3  
Footnote 1: School of Software EngineeringGenerally, more attention has been given to the structure of CNNs, and layers are added for network efficiency. [PERSON] et al. proposed a Laplacian pyramid for transforming the input images [9]. [PERSON] et al. presented a coarse-to-fine method to process multi-scale inputting images [10]. Further, an encoder-decoder structure was popular for its effectiveness, and many networks preferred to adopt such multi-scale context processing [11; 12; 13]. In addition, some networks for semantic segmentation showed effectiveness with a similar structure to that of the encoder-decoder structure [14]. The structure of CNNs has been adjusted in various methods and has become an effective way to improve the corresponding networks. Many networks were improved by adjusting various enlarging rates of various kernel sizes.