吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (4): 1353-1359.doi: 10.13229/j.cnki.jdxbgxb201504047

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Multi-target association algorithm for remote sensing images based on MSA features and simulated annealing optimization

LI Hui-hui1, HUA Li1, YANG Ning1, LIU Kun2   

  1. 1.College of Automation, Northwestern Polytechnical University, Xi'an 710072,China;
    2.School of Information Engineering, Shanghai Maritime University, Shanghai 200135, China
  • Received:2013-12-01 Online:2015-07-01 Published:2015-07-01

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Abstract: Since the current remote sensing imaging techniques can only get sparse sampling remote images, which are unable to accurately estimate the target state information, so the traditional association method using state features is not suitable for remote sensing image target. If the time independent image features are selected as the associative volume, the ambiguity induced by the association multiple targets in big scene can not be handled. To solve the above problem, a multi-scale autoconvolution feature matching and simulated annealing optimization algorithm is proposed. First, the MSA features are extracted and the matching probability between features is calculated to construct the whole association cost matrix. Then, the adaptive temperature updating function and the double threshold are set to improve the simulated annealing algorithm for quick search of the global optimal solution. Experimental results show that the proposed algorithm can effectively use the feature information of remote sensing image, eliminate association ambiguity and solve the problem of multi-target association efficiently.

Key words: photogrammetry and remote sensing, target association, msa feature, association cost matrix, simulated annealing algorithm

CLC Number: 

  • TP751
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