Vehicle Classification Based on Seismic Signatures Using Convolutional Neural Network
Seismic signals can be used for vehicle classification. However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2019-04, Vol.16 (4), p.628-632 |
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| creator | Jin, Guozheng Ye, Bin Wu, Yezhou Qu, Fengzhong |
| description | Seismic signals can be used for vehicle classification. However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates the application of CNN to classify vehicles by means of the seismic trace that the geophone recorded. The study has two primary contributions. First, a deep CNN architecture for vehicle classification by seismic signal is proposed. Second, considering the similarities between speech recognition and vehicle classification based on seismic signal, log-scaled frequency cepstral coefficient (LFCC) matrix is proposed to extract features of seismic signals as the input of CNN. The data from DARPA's SensIt project, which contain seismic signals from two kinds of vehicles, are used to evaluate the method. By combining the proposed LFCC matrix and CNN architecture, the algorithm produces a state-of-the-art result compared with other methods. |
| doi_str_mv | 10.1109/LGRS.2018.2879687 |
| format | Article |
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However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates the application of CNN to classify vehicles by means of the seismic trace that the geophone recorded. The study has two primary contributions. First, a deep CNN architecture for vehicle classification by seismic signal is proposed. Second, considering the similarities between speech recognition and vehicle classification based on seismic signal, log-scaled frequency cepstral coefficient (LFCC) matrix is proposed to extract features of seismic signals as the input of CNN. The data from DARPA's SensIt project, which contain seismic signals from two kinds of vehicles, are used to evaluate the method. By combining the proposed LFCC matrix and CNN architecture, the algorithm produces a state-of-the-art result compared with other methods.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2018.2879687</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Architecture ; Artificial neural networks ; Classification ; Computer architecture ; Convolution ; convolutional neural network (CNN) ; Convolutional neural networks ; Data processing ; Feature extraction ; log-scaled frequency cepstral coefficients (LFCCs) matrix ; Mel frequency cepstral coefficient ; Neural networks ; Seismic activity ; Seismic analysis ; seismic signal ; Signal classification ; Speech recognition ; Vehicles</subject><ispartof>IEEE geoscience and remote sensing letters, 2019-04, Vol.16 (4), p.628-632</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-bff4fb6c99b897065d6bf96df6afd4b5ad1ed4dedc6359613e2ac926aabc38c93</citedby><cites>FETCH-LOGICAL-c359t-bff4fb6c99b897065d6bf96df6afd4b5ad1ed4dedc6359613e2ac926aabc38c93</cites><orcidid>0000-0003-1635-4623</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8558568$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8558568$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jin, Guozheng</creatorcontrib><creatorcontrib>Ye, Bin</creatorcontrib><creatorcontrib>Wu, Yezhou</creatorcontrib><creatorcontrib>Qu, Fengzhong</creatorcontrib><title>Vehicle Classification Based on Seismic Signatures Using Convolutional Neural Network</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>Seismic signals can be used for vehicle classification. However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates the application of CNN to classify vehicles by means of the seismic trace that the geophone recorded. The study has two primary contributions. First, a deep CNN architecture for vehicle classification by seismic signal is proposed. Second, considering the similarities between speech recognition and vehicle classification based on seismic signal, log-scaled frequency cepstral coefficient (LFCC) matrix is proposed to extract features of seismic signals as the input of CNN. The data from DARPA's SensIt project, which contain seismic signals from two kinds of vehicles, are used to evaluate the method. By combining the proposed LFCC matrix and CNN architecture, the algorithm produces a state-of-the-art result compared with other methods.</description><subject>Algorithms</subject><subject>Architecture</subject><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Computer architecture</subject><subject>Convolution</subject><subject>convolutional neural network (CNN)</subject><subject>Convolutional neural networks</subject><subject>Data processing</subject><subject>Feature extraction</subject><subject>log-scaled frequency cepstral coefficients (LFCCs) matrix</subject><subject>Mel frequency cepstral coefficient</subject><subject>Neural networks</subject><subject>Seismic activity</subject><subject>Seismic analysis</subject><subject>seismic signal</subject><subject>Signal classification</subject><subject>Speech recognition</subject><subject>Vehicles</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFFLwzAUhYMoOKc_QHwp-NyatE2aPGrRKQwF58S3kCY3M7NrZ9Iq_ntbN3w65-E7l8uH0DnBCSFYXM1nz4skxYQnKS8E48UBmhBKeYxpQQ7HntOYCv52jE5CWGOc5pwXE7R8hXena4jKWoXgrNOqc20T3agAJhrKAlzYOB0t3KpRXe8hRMvgmlVUts1XW_cjreroEXr_F9136z9O0ZFVdYCzfU7R8u72pbyP50-zh_J6HuuMii6urM1txbQQFRcFZtSwygpmLFPW5BVVhoDJDRjNBp6RDFKlRcqUqnTGtcim6HJ3d-vbzx5CJ9dt74d_gkzJYIGnhPOBIjtK-zYED1Zuvdso_yMJlqM9OdqToz25tzdsLnYbBwD_PB-MUsazX1BabbY</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Jin, Guozheng</creator><creator>Ye, Bin</creator><creator>Wu, Yezhou</creator><creator>Qu, Fengzhong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates the application of CNN to classify vehicles by means of the seismic trace that the geophone recorded. The study has two primary contributions. First, a deep CNN architecture for vehicle classification by seismic signal is proposed. Second, considering the similarities between speech recognition and vehicle classification based on seismic signal, log-scaled frequency cepstral coefficient (LFCC) matrix is proposed to extract features of seismic signals as the input of CNN. The data from DARPA's SensIt project, which contain seismic signals from two kinds of vehicles, are used to evaluate the method. 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| subjects | Algorithms Architecture Artificial neural networks Classification Computer architecture Convolution convolutional neural network (CNN) Convolutional neural networks Data processing Feature extraction log-scaled frequency cepstral coefficients (LFCCs) matrix Mel frequency cepstral coefficient Neural networks Seismic activity Seismic analysis seismic signal Signal classification Speech recognition Vehicles |
| title | Vehicle Classification Based on Seismic Signatures Using Convolutional Neural Network |
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