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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Efficient Detection of Intensively Parked Vehicles Form Satellite Image with 0.5-Meter Spatial Resolution
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Efficient Detection of Intensively Parked Vehicles Form Satellite Image with 0.5-Meter Spatial Resolution

机译：从0.5米空间分辨率的卫星图像有效检测密集停车的车辆

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摘要

It is challenging to distinguish the intensive parked vehicles in the parking lot for the high-resolution satellite images even at a spatial resolution of 0.5 meter. A vehicle segmentation and detection algorithm is proposed to address this problem based on the improved Mean Shift algorithm with the adaptive parameter adjustment. The suspected vehicle areas are first located using the regional growth with the seed point determined by the simplified Itti algorithm and non-maximal suppression. An adaptive parameter adjustment based Mean Shift method is presented to divide each suspected vehicle area into the suspected vehicle slices. To reduce the false alarms, feature vectors contained spectral and texture features are constructed and used in the classification based on support vector machine to distinguish between vehicles and non-vehicles. Experimental results on several remote sensing images at a spatial resolution of 0.5 meter present the accurate detection results for the intensive parked vehicles.
机译：即使在空间分辨率为0.5米的情况下，也很难区分停车场中密集的停放车辆以获取高分辨率卫星图像。提出了一种基于自适应参数调整的改进Mean Shift算法的车辆分割与检测算法。首先使用区域增长来定位可疑车辆区域，并通过简化的Itti算法和非最大抑制来确定种子点。提出了一种基于自适应参数调整的均值漂移方法，将每个可疑车辆区域划分为可疑车辆切片。为了减少误报，构建了包含光谱和纹理特征的特征向量，并在基于支持向量机的分类中将其用于区分车辆和非车辆。在空间分辨率为0.5米的几个遥感影像上的实验结果显示了密集停车车辆的准确检测结果。

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