Aircraft target recognition in remote sensing image combined saliency map with deep learning

doi:10.13474/j.cnki.11-2246.2019.0072

Abstract

Abstract: In order to identify the aircraft target in high resolution remote sensing image accurately and quickly,a kind of aircraft target recognition algorithm combined the saliency map with the deep belief networks is proposed.First,the salient object in the image is extracted by use of histogram-based contrast method;Second,the candidate target is located by locating connected region;Then the color moment,Hu invariant moments,Tamura texture features and the edge direction histogram of candidate target is extracted.Last,the normalized feature is applied to deep belief networks to recognize the target.Experimental results show that the detection rate of the algorithm is 98.46% and the false alarm rate is 5.20%.Multi-feature provides more information than single-feature,and the ability of target recognition and anti-interference is improved.

Key words: aircraft targets recognition, saliency map, multi-feature fusion, deep belief networks, remote sensing image

CLC Number:

P237

LIU Xiangyun, GONG Zhihui, JIN Fei, YANG Guang, FAN Weikang. Aircraft target recognition in remote sensing image combined saliency map with deep learning[J]. 测绘通报, 2019, 0(3): 27-31.

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