| 英文摘要 | Along with the rapid advance in remote sensing technology, the data of remote sensing image is getting bigger and bigger, as a result, the real-time processing for large remote sensing images within limited computing resources is becoming a hot research topic gradually. Recently, cognitive psychology discovered that, when facing complex scenes, human visual system can rapidly pay attention to few salient regions and deal with the corresponding information preferentially. The process which aims at obtaining salient regions is called saliency detection. By means of saliency detection technology, valuably concerned visual scenes are offered while a large proportion of irrelevant part are sieved, which results in the obvious improvement of image processing efficiency. In the exciting methods of saliency detection, the bottom-up mechanism is usually applied, and the corresponding salient region is captured by data driving. These exciting methods can efficiently highlight the areas which have strong response to the low-level vision stimuli, however, since the priori information of the target is not concerned in these methods, it is unavailable to obtain the valuably interested area which is correlative with targets only by means of the underlying data. Recently, top-down saliency detection method is becoming a hot research field, by this method, more interested area correlative with targets can be captured. Top-down method includes two following models, perceptual model and calculation model. In the perceptual model, top-down mechanism is introduced by adjusting the weight factor of low-level vision feature. This model can be employed to deal with the natural image efficiently, however when facing the remote sensing image with complex scene, it is difficult to distinguish the target from its background only by means of adjusting the corresponding weight factor. While in calculation model, the saliency of each area is measured via extracting salient features. Compared with the perceptual model, since the image feature with strong discriminant is employed in this method, higher detection accuracy can be obtained, and the target can be focused more strongly. However, in case of the large remote sensing image, it is a time-consuming process to extract salient feature, as a result, this method can not satisfy the requirement of real-time processing. Additionally, in order to capture the weight of feature maps or salient features, a large number of training sampl... |
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