Oil Tank Detection Based on Linear Clustering Saliency Analysis for Synthetic Aperture Radar Images
Libao Zhang, Congyang Liu · 2019
As the significant position of oil tanks in oil storage, oil tank detection plays an important role in auto target detection for SAR images. This paper presents a linear clustering saliency analysis based detection model. Firstly, a linear iterative clustering of which the feature vector consists of three texture features and 2-D coordinates is used to over segment the input image. At the same time, multi intensity saliency maps constrain the shape of the superpixel using an adaptive balance weight. Secondly, feature vectors generated from the cluster centers are scattered as far as possible via Principal Component Analysis and sent to the MeanShift model to coarsely locate the candidate area. Finally, strong scattered points on the roof of tanks are utilized to locate the top of the targets. The whole method is evaluated in two aspects: the evaluation of saliency analysis and the accuracy rate of the top location. Experiment shows the efficiency and superiority of our algorithm with fewer interferences and more accurate location of oil tanks.