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Lidar detection of underwater objects using a neuro-SVM-based architecture
- Source :
- IEEE Transactions on Neural Networks. 17:717-731
- Publication Year :
- 2006
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2006.
-
Abstract
- 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.
- Subjects :
- Backscatter
Computer Networks and Communications
Computer science
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Information Storage and Retrieval
Linear prediction
Sensitivity and Specificity
Pattern Recognition, Automated
law.invention
Naive Bayes classifier
Imaging, Three-Dimensional
Artificial Intelligence
law
Image Interpretation, Computer-Assisted
Computer vision
Radar
Physics::Atmospheric and Oceanic Physics
Artificial neural network
business.industry
Lasers
Reproducibility of Results
Pattern recognition
General Medicine
Quadratic classifier
Image Enhancement
Object detection
Computer Science Applications
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Lidar
Neural Networks, Computer
Artificial intelligence
business
Algorithms
Software
Subjects
Details
- ISSN :
- 19410093 and 10459227
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks
- Accession number :
- edsair.doi.dedup.....2305d0408a818cea8d4552f4d7b76d95