Translator Disclaimer
7 September 2018 Saliency and density enhanced region-of-interest extraction for large-scale high-resolution remote sensing images
Author Affiliations +
The region of interest (ROI) extraction is of crucial importance in the preprocessing of object detection, especially when the spatial resolution of the remote sensing image becomes extremely high and the field of view becomes relatively large. To conduct the detection approaches directly on the image usually yields unsatisfactory result, and is time consuming. Saliency models based on visual attention mechanism are the general solution to this problem. However, the conventional saliency models deal with the pixel intensity, color statistics or contrast, while neglect the characteristics and spatial distribution of the ROI, which would results in the false alarm in the extraction. In this paper, taken residential area as the region of interest, a ROI extraction method based on saliency, and enhanced by corner density is proposed. The saliency model is adopted to extract the potential area preliminarily. In spite of the efficiency of the model, it suffers from certain defect, that is, the preliminary extracted region contains plenty of false alarms due to the high contrast of bare land and water reflection. Therefore, corner density feature is constructed to refine the extraction, based on the idea of residential area showing higher edge and corner density compared to rural area. In the experimental part, the proposed method is compared with three saliency models. The experimental results reveal that the proposed method is effective in eliminating the false alarm caused by high intensity or contrast of the pixel.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tong Li, Junping Zhang, Qingle Guo, and Bin Zou "Saliency and density enhanced region-of-interest extraction for large-scale high-resolution remote sensing images", Proc. SPIE 10764, Earth Observing Systems XXIII, 107641X (7 September 2018);

Back to Top