IIIT Hyderabad Publications |
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Adaptive Spectral and Texture matching for road extraction from very high resolution satellite imagery with occlusion handlingAuthor: K Sreekanth Reddy Date: 2017-04-01 Report no: IIIT/TH/2017/8 Advisor:K S Rajan AbstractRoad extraction from very high (VHR) and high resolution (HR) satellite imagery is one of the important problems in the remote sensing area. Road network database is one of the essential features for various GIS applications like traffic management, transportation management and urban planning. Generally the road map is generated using the on-field survey techniques and GPS (Global Positioning System) based tracking methods. But recently the techniques which uses the remote sensing aerial and satellite imagery for road extraction are being developed. Since road region appear as linear segment in low resolution images, earlier research on road extraction focused on extracting road center-line from low resolution images. On the other hand, very high resolution satellite images provide an opportunity to extract entire road area along with road network. However, factors like variation in road surface characteristics and changing road geometry (widths and curvatures), occlusion due to trees building shadows and vehicles, the neighbouring non-road regions which become more apparent in very high resolution satellite images, make road extraction a challenging problem. Difference in the surface characteristics of the road in an image become too obvious in VHR images. Because of this complexity of the sensor and ground characteristics it makes difficult to have an automatic road technique which may extract the road area well in some scenarios but fails in most of the cases. In those cases semi automatic techniques where user provides some intial seeds which represents the road surface characteristics can be helpful in solving the issue. In this thesis a semi automatic road extraction algorithm based on adaptive spectral and texture matching (ASTM-R) with occlusion handling, which is a variant of region growing approach is presented. The algorithm developed in this thesis is robust to variations in radiometric resolution of input images, road surface characteristics and road geometry (widths and curvatures) and is able to reduce the false detection of neighbouring non-road regions. In addition to that, the proposed algorithm here can jump over the occlusions and can proceed with road tracking, resulting in using minimum number of user given seeds by being able to handle the occluded areas. The algorithm is tested on various satellite images, which comprises of PAN-sharpened QuickBird, IKONOS, Orbview3 and WorldView-2 images. Images are chosen to include road regions with varying width, curvatures, different ground characteristics. Experiments are conducted on images with disconnected road segments, road regions occluded with trees, vehicles. The measures used to evaluate the algorithm performance are road area completeness, correctness of road area and road network completeness. The algorithm is able to consistently extract 70% to 90% of the road area completeness and correctness. Reduction in the number of seeds is the another measure used in order to evaluate the occlusion handling performance of the algorithm. The algorithm is able to handle the occlusions and has considerably reduced manual intervention in road extraction process by minimizing 60-80% of the user seeds. Full thesis: pdf Centre for Spatial Informatics |
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