Intelligent road extraction from high resolution remote sensing images based on optimized SVM

Road recognition and extraction based on high-resolution remote sensing images is currently a hot issue at the forefront of image processing and other disciplines, and its results not only help to enrich geographic information, but also have important application value in national defence construction and other aspects. For the intelligent extraction of roads in high-resolution remote sensing images, the study firstly preprocesses these images, and then extracts the roads by using variational function and support vector machine. As the extraction results are not satisfactory enough, the 3D wavelet transform technique is used to extract the spectral features of roads in the frequency domain, combined with the texture in the time domain, and optimized the support vector machine in order to extract the road images more accurately. The outcomes denote that in contrast with other methods, the optimized method can have optimal correctness and quality while guaranteeing higher integrity. The satellite road image of Chengdu A industrial park is rich in extracted information, having the highest completeness of 94.23%, correctness of 60.73%, and quality of 59.42%, and the satellite road image of Chengdu B industrial park is similarly rich in extracted information, having the highest completeness of 83.47%, correctness of 85.31%, and quality of 72.85%. The highest value of CPU usage for road extraction was 2000 kb at 37 s, and the average value of CPU usage for the whole 60 s was 500 kb. It shows that the research method has good results in high resolution remote sensing image road extraction.