A Feature Extraction Method for Wheeled and Tracked Vehicle Classification Based on Geologic Model

Qianwei Zhou,Baoqing Li,Dongfeng Xie,Zhijun Kuang, Xiaobin, Yuan,Qianwei Zhou,Baoqing Li,Dongfeng Xie,Xiaobin Yuan

Computational and Information Sciences（2013）

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摘要

Seismic signal is widely used in ground vehicle classification due to its inherent characteristics. But the generalization accuracy of classifier is heavily degraded due to different underlying geologies. To overcome the weakness of the seismic signal, a feature extraction method is proposed in this paper. The extracted feature is the cepstrum of the seismic signal whose logarithmic power spectrum density will be preprocessed to suppress the geology related components, which is based on the special characteristics of the employed geologic model, before further calculations. The efficiency of the proposed feature is verified with a mixed database taking from our field experiments and SensIT project.

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关键词

inherent characteristic,wheeled vehicle classification,database management systems,mixed database,geology,field experiment,sensit project,tracked vehicles,generalization accuracy,geologic model,feature extraction method,greens function fethod,cepstral analysis,cepstrum,wheeled and tracked vehicle classification,feature extraction,seismic signal,geophysical signal processing,signal classification,ground vehicle classification,logarithmic power spectrum density,different underlying geology,geology related components,tracked vehicle classification,proposed feature,accuracy,databases

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