Research On Road Extraction Of Remote Sensing Image

People can get more precise, rich and comprehensive information from high-resolution remote sensing image. The goal of the application of remote sensing image is getting information from remote sensing image, accessing knowledge by identifying the interested target, and at last understanding the image. With constantly update of the roads imformation, the traditional manual method can not meet demand, so the remote sensing technology, electronic technology and image recognition technology were combined to research the automatic extraction of the roads in remote sensing images. The research has great significance on road monitoring, GSP navigation and map update, and it also is the focus in the world.The paper researched road extraction methods of remote sensing image. Firstly, the classification of road remote sensing images in online database was researched; and then studied the automatic identification methods of remote sensing image of clouds in order to avoid clouds over the roads in the remote sensing image; at last, two methods were used to extract roads in remote sensing images. The classification method of road remote sensing images based on test and image information fusion was proposed to identify the road remote sensing image in the online remote sensing image database. The method extracted text features and image features, then made the features of both fusion, trained the features by SVM, and it would get better results by this method. Four texture feature parameters (angular second moment, contrast, correlation and entropy) are chosen to extract out to identify the remote sensing image of clouds. Structure tensor was used to calculate the main direction of the remote sensing image of road, and the improved calculation method was also proposed, and then connected with Gibbs sampling to extract the road in the image. This method was applicable to sheltered road, but can not explain the importance of the road. Then the circular projection transformation was used to extract roads, and this method can find the optimal template by matching the initial template to extract roads, it also can ixplain the importance of the road.The main research contents are as follows: (1) The road remote sensing image recognition based on fusing text and image information. The conventional method can not identify the road remote sensing image directly because the images in the online image database are not measured in the same laboratory environment and used with the same technical parameters. Road remote sensing images and their comments in the online image database can be used to get high precision road remote sensing image recognition results. Space pyramid keywords histogram was used to describe the features of the images, and the fusion of image information and text information can improve the recognition accuracy of the road remote sensing images. The information getted from online image database were trained by SVM to get better classification accuracy. At last, the final results and posterior probability values of SVM can be getted by integrating all the information. This method had better recognition accuracy and classification performance compared with using a separate image features or separate text features to identify the images.(2) Automatic recognition of remote sensing image of clouds based on texture features. The automatic identification method based on texture features was proposed for the high-resolution remote sensing image of clouds. The texture features of clouds and surface in images were statistical analyzed by gray level co-occurrence matrix; and then four texture feature parameters (angular second moment, contrast, correlation and entropy) of clouds and surface are chosen for the recognition of images; finally, the results was corrected by the clouds recognition method of image spatial domain.(3) Road extraction in remote sensing image based on structure tensor. The calculation method of main direction of road was improved by combined improved Gaussian filtering and Canny operator for the purpose of solving the results were not precise by the primitive method; and then extracted the road in the remote sensing image by combining local main directions and Gibbs sampling. This method can be more precise on the road extraction.(4) Road extraction in remote sensing image based on circular projection transformation. The method of road extraction in remote sensing image based on structure tensor can not explain the importance of the roads. Circular projection transformation can be used to extract roads in remote sensing images, and the method can get more precise result, at the same time, the importance of the roads can be ovserved from the result images.