中文

Journal of Applied Sciences ›› 2010, Vol. 28 ›› Issue (4): 374-380.doi: 10.3969/j.issn.0255-8297.2010.04.008

• Signal and Information Processing • Previous Articles     Next Articles

Seabed Classification Based on Principal Component Analysis of Multiple Features Combined with Sonar Image

MA Fei-hu1;2, SUN Cui-yu1;3, KANG Yong-hong1, LIU Zhi-min3   

  1. 1. School of Civil Engineering and Architecture , East China Jiaotong University, Nanchang 330013, China
    2. Key Laboratory of Precise Engineering and Industry Surveying, State Bureau of Surveying and Mapping, Wuhan 430079, China
    3. Geomatics College, Shandong University of Science and Technology, Qingdao 266510, Shandong Province, China
  • Received:2010-03-31 Revised:2010-05-20 Online:2010-07-23 Published:2010-07-23

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Abstract:

Aimed at seabed classification, statistical characteristics are extracted from the echo, and a full feature vector is constructed. The principal component analysis (PCA) is carried out to obtain the set of characteristics that most contribute to the classification. Based on the study, seabed classification is carried out and tested with two sets of experiments. Using two types of classification methods to analyze, the data from Jiaozhou Bay and comparison is made. It is concluded that the result based on PCA of many features combined with a sonar map is better than that obtained solely from sonar image classification.

Key words: feature extraction, full feature vector, principal component analysis, seabed classification

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