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Built-up area has been one of the most important objects to be extracted in remote sensing images. Several factors such as complex structure, diverse texture and varied background, bring the challenges for the task of built-up area extraction. In this paper, a multiple input structure of deep convolution neural network (CNN) is proposed to extract built-up area automatically, which can fuse the information...
Automatic built-up areas detection from remote sensing images has attracted considerable research interest, due to its crucial roles in various applications. As far as built-up areas detection, the corner density map to predict the presence of the built-up areas has been widely adopted, but the calculation is generally time-consuming. In addition, the density map is just segmented by a statistical...
In this paper, a new adaptive and fast Constant false alarm rate (CFAR) target detection algorithm based on two level CFAR (TL-CFAR) detectors in high-resolution synthetic aperture radar (SAR) images is proposed. In the first level, the initial mask of targets is obtained by Cell Averaging CFAR (CA-CFAR) detector. In the second level, the precise parameters estimation of CFAR in the local window is...
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