A Novel Target-Objected Visual Saliency Detection Model in Optical Satellite Images

Abstract

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