Utilizing Elephant Herd-Inspired Spiking Neural Networks for Enhanced Ship Detection and Classification in Marine Scene Matching

Utilizing Elephant Herd-Inspired Spiking Neural Networks for Enhanced Ship Detection and Classification in Marine Scene Matching

In the realm of marine intelligence, effective ship classification across vast oceanic expanses is pivotal. Despite strides in conventional identification techniques, existing methods exhibit limitations in terms of effectiveness, robustness, and overall performance. This paper introduces a novel El...

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Bibliographic Details
Published in:Marine geodesy Vol. 47; no. 6; pp. 503 - 525
Main Authors: Sayed, Abdhahir Y, Senthil Singh C
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 01-11-2024
Taylor & Francis Ltd
Subjects:
Accuracy
Algorithms
Classification
Deep learning
Effectiveness
Elephant Herd algorithm
Elephants
Feature extraction
Firing pattern
Fog
Honing
Machine learning
mobile net
Neural networks
Retinex (algorithm)
Satellite imagery
Sea vessels
Ship classification
Ships
Spacecraft recovery
Spiking
spiking neural network
Wavelet transforms
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Summary:In the realm of marine intelligence, effective ship classification across vast oceanic expanses is pivotal. Despite strides in conventional identification techniques, existing methods exhibit limitations in terms of effectiveness, robustness, and overall performance. This paper introduces a novel Elephant Herd optimization based Spiking Neural Networks (EHO-SNN) for discerning large ships, small vessels, and the absence of ships. Initial satellite images from the Airbus dataset capture ships at sea, subjected to preprocessing via the wavelet transform-based Retinex algorithm (WRA) to eliminate noise and fog artifacts. Deep learning mobile net facilitates feature extraction, while the Elephant Herd algorithm culls irrelevant features, honing in on the most pertinent ones. Finally, the classification through a spiking neural network, distinguishing between large ships, small vessels, and the absence of ships. Detected large and small ships are accurately positioned within a selected scene, while the absence of a ship terminates the process. The Proposed EHO-SNN model attains an impressive classification accuracy of 99.10%. Notably, it surpasses OMRCNN-SHD, Efficient Net, and AN-YOLOv4 by 1.15%, 12.30%, and 7.79%, respectively, thereby advancing overall accuracy in ship classification.
ISSN:0149-0419
1521-060X
DOI:10.1080/01490419.2024.2392121