To extract circular array targets from high-spatial resolution optical satellite images, we propose an efficient two-step detection framework. In the first stage, we propose a multiscale histogram contrast (MHC) saliency evaluation model to detect salient objects. This saliency model is able to highlight all salient areas with well-defined boundaries and effectively suppress the obvious background textures. Moreover, the generated saliency map can be used to find the locations of potential targets most likely causing visual attention, which greatly decreases the computation cost in later processes. In the second stage, we extract task-related targets from these salient objects by removing the irrelevant salient areas according to the special properties of the targets. In this paper, we present a circular array targets-detection method based on the saliency map produced by the proposed saliency detector. First we extract the circular objects from the saliency map using simple shape features, which are then clustered through a graph-based approach to locate the target areas. To evaluate the performance of our saliency model and targets extraction scheme, we carried out many experiments on large-field, high-spatial resolution remote-sensing images and achieved promising results both in efficiency and precision.
In this paper, we propose a novel framework of building extraction from high resolution satellite remote sensing images.
This approach combines regional information with edge features, which could avoid the limitations of traditional
methods. Firstly, the irrelevant regions are filtered out from large images by extracting texture features, leaving buildings
distributed in the remaining urban areas. Secondly, boundaries of objects are extracted and all the contours are tracked
through the chain code method we developed. Finally, the short straight lines are linked to construct contours and extract
rectangular building rooftops. Experiments show that this extraction strategy is robust and effective.