A Novel System for Seafloor Classification Using Artificial Neural Network (ANN) Hybrid Layout with the Use of Unprocessed Multi-Beam Backscatter Data

Acces sibility Controls

Implementing Organization

National Institute of Oceanography (NIO)

About

The novel system for seafloor classification uses artificial neural network (ANN) hybrid layout with the use of unprocessed multi-beam backscatter data. Its a real-time seafloor roughness classifier using backscatter data after training the self-organized mapping (SOM) network and learning vector quantization (LVQ) network wherein, the system has the unique capability for the combined use of unsupervised SOM followed by supervised LVQ to achieve a highly improved performance in the roughness classification.

Outcome / Output

Combined use of the two variants of the learning vector quantization (LVQ) network to achieve the best classification of the seafloor characteristics, which is a capability that is hitherto non-existent , Self-organization of multi-beam input data vectors into coarse clusters in the output space without any a priori information , Raw dataset can be used as input vectors to the classification network , Reduces computational time overhead

Source

Source URL

technologies/novel-system-seafloor-classification-using-artificial-neural-network-ann-hybrid-layout-use

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Quick Information

Thrust Areas

Physical Sciences

Focus Area

Seafloor Classification

Status

completed

Contact

vsnmurty@nio.org

Acces sibility Controls

Physical Sciences