论文标题
在卫星视频中学习车辆检测
On Learning Vehicle Detection in Satellite Video
论文作者
论文摘要
与遥感图像的整体尺寸相比，由于它们在像素中的微小外观，空中和卫星图像中的车辆检测仍然具有挑战性。在这种情况下，经典的对象检测方法经常由于违反了诸如丰富纹理的隐式假设，图像大小和对象大小之间的比例小比例而失败。卫星视频是一种非常新的方式，它引入了作为感应偏见的时间一致性。卫星视频中车辆检测的方法使用背景减法，框架差异或子空间方法显示中等性能（0.26-0.82 $ f_1 $得分）。这项工作建议在卫星视频上应用有关大区域运动图像（WAMI）的最新研究。我们在第一种方法中显示了Planet的Skysat-1 lasvegas视频的可比较结果（0.84 $ f_1 $），并带有可进一步改进的空间。
Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moderate performance (0.26 - 0.82 $F_1$ score). This work proposes to apply recent work on deep learning for wide-area motion imagery (WAMI) on satellite video. We show in a first approach comparable results (0.84 $F_1$) on Planet's SkySat-1 LasVegas video with room for further improvement.
