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A target-oriented visual saliency detection model for optical satellite images is proposed in this paper. This model simulates the structure of the human vision system and provides a feasible way to integrate top-down and bottom-up mechanism in visual saliency detection. Firstly, low-level visual features are extracted to generate a low-level visual saliency map. After that, an attention shift and selection process is conducted on the low-level saliency map to find the current attention region. Lastly, the original version of hierarchical temporal memory (HTM) model is optimized to calculate the target probability of the attention region. The probability is then fed back to the low-level saliency map in order to obtain the final target-oriented high-level saliency map. The experiment for detecting harbor targets was performed on the real optical satellite images. Experimental results demonstrate that, compared with the purely bottom-up saliency model and the VOCUS top-down saliency model, our model significantly improves the detection accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel top-down visual saliency detection method for optical satellite images using local adaptive regression kernels. This method provides a saliency map by measuring the likeness of image patches to a given single template image. The local adaptive regression kernel (LARK) is used as a descriptor to extract feature and compare against analogous feature from the target image. A multi-scale pyramid of the target image is constructed to cope with large-scale variations. In addition, accounting for rotation variations, the histogram of kernel orientation is employed to estimate the rotation angle of image patch, and then comparison is performed after rotating the patch by the estimated angle. Moreover, we use the bounded partial correlation (BPC) to compare features between image patches and the template so as to rapidly generate the saliency map. Experiments were performed in optical satellite images to find airplanes, and experimental results demonstrate that the proposed method is effective and robust in complex scenes. [ABSTRACT FROM AUTHOR]