Research on Road Extraction from High Resolution Remote Sensing Images Integrating SVM in Landscape Design
To improve the accuracy and effectiveness of road extraction from high-resolution remote sensing images in landscape design,a road extraction method based on SVM fusion is proposed.Firstly,the method combines Mean Shift algorithm and mathemat-ical morphology operation(MS-MMO for short)to extract shadow from images;Then,based on the shadow extraction results,the brightness of the shadow area in the original image is compensated and input into SVM to obtain the preliminary extracted road image;Then,the Gaussian filter algorithm is used to smooth the image,and the edge filter,texture filtering and other algorithms are used to remove the non road areas in the image to obtain the road region extraction image;Finally,the road centerline is extracted based on tensor voting,and the burrs on the road centerline are removed using the"intersection"search method to complete the road extrac-tion.The experimental results show that MS-MMO has good shadow extraction accuracy and effectiveness;After compensating the brightness of the shadow area in the original image based on the shadow extraction results output by MS-MMO,the overall perform-ance of road extraction is higher;The road integrity,accuracy,and quality extracted by the high-resolution remote sensing image road extraction method fused with SVM reached 92.4％,92.7％,and 89.0％,respectively.The road extraction performance is good,and the roads have connectivity properties.Landscape design on the road images extracted by this method can effectively improve the effectiveness of road plant configuration.
mean shift algorithmmathematical morphology operationsupport vector machineroad extractionfiltering algo-rithm
万方数据
维普
