Oil Depots Recognition Based on Improved Hough Transform and Graph Search
基于改进Hough变换和图搜索的油库目标识别

In order to identify the circular oil depots from remote sensing images, a developed Hough transform method based on gradient information is proposed to extract circular oil tanks firstly. Then, the depth-first graph search strategy is employed to group the detected circles and eliminate the false alarms according to the spatial distribution of the oil depots. Finally, the target areas of oil depots are localized. The improved Hough transform reduces the time and space consumption by using the gradient direction information and reducing the dimension of parameter space, and improves the efficiency of circles detection. The graph search strategy can exclude the false targets and locate the target areas, which improves identification accuracy. The experimental results indicate that the proposed algorithm can recognize the oil depots targets fast and accurately, which is suitable for optical remote sensing images of different spatial resolutions.