Hierarchical Detection from Parking Lot to Vehicle in Large-Area Remote Sensing Images Based on Visual Saliency and Angle Estimation

Hierarchical detection from parking lot to vehicle based on visual saliency and angle estimation is proposed for the large-area remote sensing image (RSI). According to the brightness characteristics of parking lot, an accelerated bright-based saliency map (BBSM) is presented to locate the parking lot, which is also achieved by two-step location from rough to accurate. The suspected vehicle queues are then detected using the spectral residual saliency map and rotated to the horizontal direction by angle estimation of vehicle queue. Each vehicle in the suspected vehicle queues is identified by HOG features extraction and SVM classification. Using several RSIs at a spatial resolution of 0.5 meter with the size of 24000×24000 pixels, experimental results show that the proposed method outperforms two traditional methods and achieves the precision ratio of 87.1% and recall ratio of 90.2%.