Compressed Sensing Recognition Algorithm for Sonar Image Based on Non-Negative Matrix Factorization and Adjacency Spectra Feature

Yan Ling Hao; Liang Wang

doi:10.4028/www.scientific.net/AMR.383-390.5818

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 383-390Compressed Sensing Recognition Algorithm for Sonar...

Compressed Sensing Recognition Algorithm for Sonar Image Based on Non-Negative Matrix Factorization and Adjacency Spectra Feature

Abstract:

A compressed sensing (CS) recognition algorithm for sonar image based on non-negative matrix factorization and adjacency spectra (A-NMF) feature extraction is proposed in this paper. The feature vector of the tradition CS recognition algorithm is random Gaussian matrix, which causes the identification rate unstable. The stable feature vector which is extracted by the A-NMF feature extraction is used in this algorithm. The feature and structure of the original data can be expressed more accurately by this feature vector. Then the sonar images are classified under the compressed sensing framework. Experimental results show that the sonar image recognition is high, efficient and stable.

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Manufacturing Science and Technology, ICMST2011

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Periodical:

Advanced Materials Research (Volumes 383-390)

Pages:

5818-5823

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.383-390.5818

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Online since:

November 2011

Authors:

Yan Ling Hao, Liang Wang

Keywords:

Adjacency Spectra Feature, Compressesd Sensing, Non-Negative Matrix Factorization, Sonar Image Recogition

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