A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery

TLDR: Experimental results indicate that the proposed method is robust under different complex backgrounds and has high detection rate with low false alarm.

Abstract: Automatic oil tank detection plays a very important role for remote sensing image processing. To accomplish the task, a hierarchical oil tank detector with deep surrounding features is proposed in this paper. The surrounding features extracted by the deep learning model aim at making the oil tanks more easily to recognize, since the appearance of oil tanks is a circle and this information is not enough to separate targets from the complex background. The proposed method is divided into three modules: 1) candidate selection; 2) feature extraction; and 3) classification. First, a modified ellipse and line segment detector (ELSD) based on gradient orientation is used to select candidates in the image. Afterward, the feature combing local and surrounding information together is extracted to represent the target. Histogram of oriented gradients (HOG) which can reliably capture the shape information is extracted to characterize the local patch. For the surrounding area, the convolutional neural network (CNN) trained in ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) contest is applied as a blackbox feature extractor to extract rich surrounding feature. Then, the linear support vector machine (SVM) is utilized as the classifier to give the final output. Experimental results indicate that the proposed method is robust under different complex backgrounds and has high detection rate with low false alarm.