Oil Tank Detection Using Co-Spatial Residual and Local Gradation Statistic in SAR Images
Libao Zhang, Congyang Liu · 2019
The advance in synthetic aperture radar (SAR) technology reduces the difficulty of data acquisition, along with greatly increased computational complexity. This paper cares about this and aims to accomplish oil tank detection in SAR images from the perspective of computer vision. The whole model includes three main steps. The first is a saliency-driven clustering to accomplish the single image saliency analysis according to the intensity specificity and texture distribution. The second step introduces a common visual saliency analysis based on the co-spatial residual and local gradation statistic to extract the common visual salient parts within the input series. The final step considers the distribution of adjacent highlight points obtained from the saliency analysis to accomplish the location of tanks. Three competing methods are established in the experimental part. The evaluation in pixel level and geometric segmentation accuracy both verify the superiority of the proposed model in target detection and interferences exclusion.