Physical Sciences


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


Physical Sciences

Focus Area:

Seafloor Classification

Social Benefits:

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

Developing Agency:

CSIR-National Institute Of Oceanography (NIO), Goa

Technology Readiness Index:

Technology Demonstration

Website Link :
Source (more info) :

Brief Description

Description :

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.

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