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A new target association algorithm based on invariant features in remote sensing images

机译:一种基于遥感图像中不变特征的新目标关联算法

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There are two problems when associating multiple targets in remote sensing images: Firstly, with low temporal resolution observation, the target's kinematic state cannot be estimated accurately and the classical Kalman filtering association algorithms are no more applicable. Secondly, the classical image feature-based target matching algorithms cannot deal with the illegibility of multiple targets' correspondence, which don't take into account the uncertainty of feature extraction. To resolve above problems, a novel multiple targets association method based on Multi-scale Autoconvolution(MSA) features matching and global association cost optimization through simulated annealing (SA) algorithm is proposed. Experiments with remote sensing images show the applicability of the method for multiple targets association.
机译:在遥感图像中关联多个目标时存在两个问题:首先,通过低时间分辨率观察,无法精确地估计目标的运动状态,并且经典的卡尔曼滤波关联算法不再适用。其次,基于古典图像特征的目标匹配算法无法处理多个目标对应关系的难忘,这不考虑特征提取的不确定性。为了解决上述问题,提出了一种基于多尺度自动调节(MSA)特征匹配和全局关联成本优化的新型多个目标关联方法通过模拟退火(SA)算法。具有遥感图像的实验显示了对多个目标关联方法的适用性。

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