{"title":"基于优化 SVM 的高分辨率遥感图像智能道路提取技术","authors":"","doi":"10.1016/j.jrras.2024.101069","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S168785072400253X/pdfft?md5=8925928e503906cfa6fb79844c256b5d&pid=1-s2.0-S168785072400253X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Intelligent road extraction from high resolution remote sensing images based on optimized SVM\",\"authors\":\"\",\"doi\":\"10.1016/j.jrras.2024.101069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":16920,\"journal\":{\"name\":\"Journal of Radiation Research and Applied Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S168785072400253X/pdfft?md5=8925928e503906cfa6fb79844c256b5d&pid=1-s2.0-S168785072400253X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Radiation Research and Applied Sciences\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S168785072400253X\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radiation Research and Applied Sciences","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S168785072400253X","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
基于高分辨率遥感影像的道路识别与提取是当前影像处理等学科前沿的热点问题,其成果不仅有助于丰富地理信息,在国防建设等方面也具有重要的应用价值。针对高分辨率遥感影像中道路的智能提取,本研究首先对这些影像进行预处理,然后利用变分函数和支持向量机对道路进行提取。由于提取结果不够理想,研究采用三维小波变换技术提取道路频域的频谱特征,结合时域的纹理特征,并对支持向量机进行优化,以更精确地提取道路图像。结果表明,与其他方法相比,优化后的方法可以获得最佳的正确性和质量,同时保证更高的完整性。成都 A 工业园区卫星道路图像提取信息丰富,完整度最高,为 94.23%,正确度最高,为 60.73%,质量最高,为 59.42%;成都 B 工业园区卫星道路图像提取信息同样丰富,完整度最高,为 83.47%,正确度最高,为 85.31%,质量最高,为 72.85%。道路提取的 CPU 占用率最高值为 37 s 时的 2000 kb,整个 60 s 的 CPU 占用率平均值为 500 kb。由此可见,该研究方法在高分辨率遥感图像道路提取方面具有良好的效果。
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.
期刊介绍:
Journal of Radiation Research and Applied Sciences provides a high quality medium for the publication of substantial, original and scientific and technological papers on the development and applications of nuclear, radiation and isotopes in biology, medicine, drugs, biochemistry, microbiology, agriculture, entomology, food technology, chemistry, physics, solid states, engineering, environmental and applied sciences.