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This paper reviews the recently developed processing techniques for remotely sensed imagery, including very high resolution (VHR) information extraction, super resolution techniques, hyperspectral image processing and object detection, and also some artificial intelligence approaches.
Linear feature extraction is an important problem for remote sensing image processing, and it is very difficult to extract those linear features embedded in strong noise or when the SNR (signal to noise) is low like the complicated environment of remote sensing image. In this paper, an algorithm based on wedgelet decomposition is proposed to extract linear… (More)
Edge detection is very useful and important for image processing and computer vision, as it can locate significant variations of gray images. In this paper, an algorithm based on beamlet transform is proposed to detect edges in image. Beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting, the beamlet transform is the… (More)