Abstract
Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management. With the development of remote sensing technology, the quality of high-resolution satellite images is improved and more easily obtained, which makes it possible to use remote sensing images to locate roads accurately. Therefore, it is an urgent problem to extract road information from remote sensing images. To solve this problem, a road extraction method based on convolutional neural network is proposed in this paper. Firstly, convolutional neural network is used to classify the high-resolution remote sensing images into two classes, which can distinguish the road from the non-road and extract the road information initially. Secondly, the convolutional neural network is optimized and improved from the training algorithm. Finally, because of the influence of natural scene factors such as house and tree shadow, the non-road noise still exists in the road results extracted by the optimized convolutional neural network method. Therefore, this paper uses wavelet packet method to filter these non-road noises, so as to accurately present the road information in remote sensing images. The simulation results show that the road information of remote sensing image can be preliminarily distinguished by convolutional neural network; the road information can be distinguished effectively by optimizing convolutional neural network; and the wavelet packet method can effectively remove noise interference. Therefore, the proposed road extraction method based on convolutional neural network has good road information extraction effect.
Highlights
Geographic information system (GIS) has been widely used in many fields
5 Conclusions With the development of science and technology, remote sensing image is more and more easy to obtain, and the extraction of road information is conducive to traffic management, urban planning, automatic vehicle navigation, and emergency handling
In order to obtain better road information of remote sensing image, a road extraction method based on convolutional neural network is proposed in this paper
Summary
Geographic information system (GIS) has been widely used in many fields. At present, the acquisition and real-time update of GIS data is the most important bottleneck problem restricting the application of GIS [1, 2]. The reduction of visual difference between high spatial resolution remote sensing image and natural image provides the condition for the application of convolutional neural network in the field of remote sensing target recognition. The large image size, image distortion, object occlusion, shadow coverage, illumination changes, and texture heterogeneity and other factors bring great challenges to the practical application of convolutional neural network in high spatial resolution remote sensing image target recognition. How to apply convolutional neural network to target recognition of high spatial resolution remote sensing image is of great theoretical significance. Convolutional neural network is applied to road information extraction from remote sensing images. 2. The convolutional neural network is used to classify the high-resolution remote sensing images into two classifications, to distinguish the road from the non-road and to preliminarily extract the road information. Wavelet packet method is used to filter out nonroad noise caused by natural scenes such as houses and tree shadows
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