结合模糊分割和遗传算法的航空影像车辆提取方法

利用高分辨率遥感影像实现道路车辆监测在交通管理系统中占有重要地位，然而受成像条件及车辆本身颜色多样性的影响，很难从遥感影像中直接提取道路车辆。为此，本文提出了一种结合遗传算法和数学形态学的遥感影像道路车辆提取方法。首先利用模糊聚类方法建立影像分割模型，并利用遗传算法对其进行求解，从而实现影像中道路、车辆及环境的精确分割；然后对分割结果中不同层进行赋值，实现分割结果的二值化；最后利用数学形态学操作从分割的道路-车辆的二值结果中剔除道路，从而实现道路车辆的提取。利用本文方法对航空影像进行了道路车辆提取试验。试验结果表明，本文方法不仅能够准确识别地面车辆位置，还能精确提取其轮廓线。

Fuzzy Segmentation and Genetic Algorithm Based Road Vehicle Extraction Method from High Resolution Aerial Image

Nowadays, vehicle object extraction of remote sensing image and its potential applications become a hot topic.However, the optical information of vehicles is seriously weakened by a series of factors, such as environment and the velocity of the vehicles, so that the extraction accuracy of vehicles is accordingly reduced.To this end,a vehicle extraction method of remote sensing image using genetic algorithm and mathematical morphological operation has been proposed.First, a pre-clustering operation of remote sensing image is done by using histogram-based technique, and optimizing the initial cluster centers applied the genetic algorithm to improve the accuracy of segmentation results.Then, the mathematical morphological operation is used to extract the edges of vehicles.The aerial images are tested to extract vehicles using the proposed approach.The experiment results show that this method can identify the situation of vehicles on the ground as well as extract the contour line accurately.