Salient Target Detection Based on the Combination of Super-Pixel and Statistical Saliency Feature Analysis for Remote Sensing Images
Libao Zhang, Yue Wang, Yang Shan Sun · 2018
The saliency analysis has become the important tool to detect the salient targets. However, due to complex target features and abundant background information interference, the traditional models are weak in salient target detection of remote sensing images. In this paper, a novel model based on the combination of super-pixel and statistical saliency feature analysis is proposed. The proposed model consists of three main steps. First, the statistical saliency feature map based on histogram statistical saliency analysis in the Lab color space is introduced. Then, information saliency feature map is obtained based on the combination of super-pixel segmentation and information entropy, and the statistical saliency feature map and the information saliency feature map are fused and enhanced to generate the final saliency map. Finally, the complete and accurate salient targets and regions of interest (ROIs) are obtained based on the improved Otsu segmentation method. Experimental evaluations show that the proposed model outperforms the state-of-the-art salient detection models.