Document Detail


Lidar detection of underwater objects using a neuro-SVM-based architecture.
MedLine Citation:
PMID:  16722175     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.
Authors:
Vikramjit Mitra; Chia-Jiu Wang; Satarupa Banerjee
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  17     ISSN:  1045-9227     ISO Abbreviation:  -     Publication Date:  2006 May 
Date Detail:
Created Date:  2006-05-25     Completed Date:  2006-06-20     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  717-31     Citation Subset:  IM    
Affiliation:
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA. vmitra@glue.umd.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artificial Intelligence*
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Information Storage and Retrieval / methods*
Lasers*
Neural Networks (Computer)
Pattern Recognition, Automated / methods*
Radar*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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