Vehicle Detection from Remote Sensing Image Based on Superpixel Segmentation and Image Enhancement

Automatic vehicle detection from high-resolution remote sensing image is a challenging topic. While there have been some studies on this topic in recent years, a fast and robust approach is yet to be found, especially when facing the scenario of low color contrast. In this paper, a new vehicle detection approach is proposed. First, superpixel-based segmentation is used to identify potential vehicle regions to speed up the detection and improve the accuracy. Then, an image enhancement method is also proposed, which greatly improves the segmentation results. Support vector machine is used to classification with features extracted by HOG descriptor. According to the experiments, by combining with superpixel segmentation and the image enhancement, the speed of the vehicle detection is improved with approximately an order of magnitude. Also, in case of low contrast remote sensing images, the detection accuracy can be also greatly improved, with much less false positives and false negatives.