首页 >  2008, Vol. 12, Issue (4) : 1993-2002

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10.11834/jrs.20080477

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基于MSA特征的遥感图像多目标关联算法
国防科学技术大学电子科学与工程学院,湖南长沙 410073
摘要:

遥感图像中多目标关联存在以下两个问题:一是低时间分辨率观测使得目标状态信息无法准确估计,基于Kalman滤波的多目标关联算法不再适用;二是基于图像特征的目标关联算法又无法处理大场景观测中多个目标关联引起的模糊性.针对上述问题,提出一种基于多尺度自卷积特征匹配和关联代价矩阵最优化的多目标关联算法.实验表明该算法对遥感图像中多目标关联问题具有一定的适用性.

A MSA Feature-based Multiple Targets Association Algorithm in Remote Sensing Images
Abstract:

Target identification fusion based onmulti-source remote sensing mi ages canmake fulluse of the redundancy and complementary information from all sensors, acquiring more accurate result of target recognition. One of the pre-condition of identification fusion is targetassociation, which is to determine if the information from two ormore mi ages are related to the same target and should be fused together. Due to different performance of sensor and diverse target distribution, the extracted information of targets generally has some uncertainty, which results in the difficulty in judging whether the information from two mi ages is originated from the same target. Therefore, how to utilize the information of remote sensing mi ages to distinguishmulti-target association has become an urgen problem.There are two kinds ofmethods concerning target association when using mi age data: one isKalman filtering based data association and tracking, which utilizes accumulated kinematic information ofmulti-frame mi ages to estmi ate and track. Typicalmethods areNearestNeighbor (NN), JointProbabilistic Data Association (JPDA), Multiple-Hypothesis Tracking (MHT) and so on. Thesemethodsneed dense sampling ofobserved data, and the targetmotionmodel should be smi ple. The otherone uses mi agematch in computervision for reference. Typicalmethods are cross correlationmatching,featurematching and so on. Thesemethods usuallywork on condition thatonly single target is concerned.For remote sensing mi ages, there are two problemswhen associatingmultiple targets in them. Firstly, it is incapable to acquire a seriesofmulti-temporal remote sensing mi ageson the same region atpresent, so the kinematic state ofa target cannotbe estmi ated accuratelywith low temporal resolution data and the classicalKalman filtering association algorithms are nomore applicable.Wemust seek for other tmi e-independent information as the associatingmeasurement, which can be mi age invariant feature.Secondly, there are two uncertainties lying in mi age feature extraction of a target. One uncertainty lies in determining invariant features due to various mi age distortions such as rotation,scaling and so on. The other lies in establishing feature correspondencesbetween any two consecutive mi ages. So, it isdifficultto discrmi inate the ambiguity ofmultiple targets correspondenceswhen using mi agematching-based associationmethod.In order to solve above problems,a novel multiple targets association method based on mi age invariant feature matching and Association CostMatrix (ACM) global optmi ization is proposed.At first,theMulti-scaleAutoconvolution (MSA) transform of a target is computed based on affine invariant theory and is used as associationmeasurement, which can overcome the negative influence of changes in target s pose,miaging viewpoint and so on. Secondly, the association costmatrix is constructed based on the dissmi ilarities ofMSA featurematching of any two target pairs from two mi ages respectively, representing the correspondence illegibility oftwo targets. Finally, theminmi alenergy ofACM is found using smi ulated annealing (SA) algorithm, and the global optmi al association result is achieved.From the smi ulation expermi ents, some conclusions can be drawn as follows: (1) Using mi age invariant features to perform target association is a validateway, overcoming the bottleneck that the tmi e-dependentkinematic feature cannotbe estmi ated from sparse remote sensing mi age series.(2)Compared with the NN local algorithm, the optmi ization of\nassociation costmatrix is a globaloptmi al algorithm and has excellentperformance in dense targets circumstance. (3)The approxmi ate algorithms such as SA can greatly mi prove the search of optmi al association costmatrix, and then make complex associationmethod practicable.

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