The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.
Target detection for hyperspectral imagery has important academic value and prospective applications both in civiland military areas. And it has been a hot spot in the areas of target recognition and remote sensing information processing. Based on the principle and research status of hyperspectral imaging, this paper surveys the potential typical applications of target detection for hyperspectral imagery. Then, the algorithm theories are summarized. In the end, the prospect of target detection of hyperspectral remote sensing images is discussed.