Computer Science ›› 2017, Vol. 44 ›› Issue (1): 289-294.doi: 10.11896/j.issn.1002-137X.2017.01.053

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High Resolution Remote Sensing Image Object Recognition Algorithm Based on SIFT and Non-parametric Bayes

WANG Jian, BAI He-xiang and LI De-yu   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Object recognition has always been a significant problem in the field of remote sensing image processing.With the development of remote sensing technology,the high resolution remote sensing images carry plenty of scale invariant features which are highly correlated with each other,and the traditional recognition methods are difficult to adapt this development.Based on the SIFT(Scale-invariant Feature Transform)algorithm,a fast and accurate algorithm for ground object recognition was proposed,namely DBSIFT(Double Backward SIFT).This method constructs the new pyramid based on SIFT,uses DP(Dirichlet Process) to identify the similar features,and then segments them.Weighing upon the relationship between geometry and arithmetic,nine indexes were selected to evaluate the accuracy of segmentation.In the experiment,similar ground object can be identified accurately,and the segmentation result’s performance is well.The effectiveness of this method is further explained.

Key words: Object recognition,SIFT,Pyramid algorithm,DP

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