Joint-Distribution And Gain Rate Based Saliency Model For Circular Tank Detection In Remote Sensing Images

Oil tank detection plays an important role in object detection for remote sensing images. While the existence of the complex cases affects the detection accuracy, this paper proposes a joint-distribution and gain rate based saliency analysis model for circular tank detection. First, the joint-feature residual is utilized to extract common parts among the selected feature maps for intensity analysis. Besides, the local gradient specificity and local flatness descriptor are introduced to assess the texture characters. Second, the introduced feature vector is used for the clustering of the input series, and the joint-distribution is utilized to label a coarse binary mask. Third, the coarse labeled mask is used to assist to calculate the gain rate for different elements of the feature vector and generate the corresponding weights to get the saliency map. Finally, the desired targets are extracted according to the local salient parts in the saliency map. Experiments are conducted on two aspects including the qualitative evaluation and the quantitative evaluation. The assessment of pixel level and geometrical segmentation shows the superiority of the proposed method compared with the listed competing algorithms.