Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligence

Explainability is critical for AI deployment in high-stakes domains: The paper demonstrates that deep learning models used in defense and security applications require transparent explanations of their decisions, not just accurate predictions. Without explainability tools like LIME, organizations cannot confidently deploy AI systems in sensitive areas like underwater threat detection.

LIME provides interpretable justifications by showing which image features drive AI decisions: The study uses LIME to explain SONAR image classifications by locally perturbing input data to reveal which visual elements the model relies on. This approach helps users understand why the AI classified an underwater object as a mine or seabed feature, rather than accepting a black-box prediction.

Transfer learning with established CNN architectures (VGG16, ResNet50, InceptionV3, DenseNet121) is effective for specialized image tasks: The research demonstrates that pre-trained convolutional neural networks can be successfully adapted for underwater SONAR image classification, suggesting that organizations don't always need to build AI systems from scratch for domain-specific applications.

This represents early work in applying XAI to underwater imaging: The authors note this is "one of the first works" applying explainable AI techniques to SONAR image analysis, indicating that responsible AI practices are still emerging in specialized technical domains and highlighting the need for continued development of transparency tools across different applications.