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As the improvement of the resolution of aerial and satellite remote sensed images, the semantic richness of the image increases which makes image analysis more difficult. Dense urban environment sensed by very high-resolution (VHR) optical sensors is even more challenging. Occlusions and shadows due to buildings and trees hide some objects of the scene. Fast and efficient segmentation of such noisy...
In this paper, a land-cover extraction thematic mapping approach for urban areas from very high resolution aerial images is presented. Recent developments in the field of sensor technology have increased the challenges of interpreting images contents particularly in the case of complex scenes of dense urban areas. The major objective of this study is to improve the quality of land-cover classification...
In this paper, we consider an invariant Generalized Hough Transform (GHT) as a shape based extractor to improve the quality of the urban land-cover classification. Dense urban environment sensed by Very High-Resolution (VHR) optical sensors is one of the most challenging problems in pattern analysis and machine intelligence systems in remote sensing. We propose a three stage framework for extracting...
Urban land-cover classification is one of the most challenging problems in pattern analysis and machine intelligence systems in remote sensing. Dense urban environment sensed by very high-resolution (VHR) optical sensors is even more challenging. Occlusions and shadows due to buildings and trees hide some objects in the scene. Despite its simplicity and usefulness, conventional classification methods...
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