Abstract :

Military Supply Chain Management Requires Efficient Decision-making Under Uncertain And Dynamic Conditions. Satellite-based Synthetic Aperture Radar (SAR) Images Provide Valuable Real-time Information For Monitoring Terrain, Infrastructure, And Logistics Movement. However, Extracting Meaningful Insights From SAR Images Is Challenging Due To Noise, Complexity, And Lack Of Interpretability In Traditional AI Models. This Project Proposes An Explainable Artificial Intelligence (XAI)-based Framework For Optimizing Military Supply Chain Operations Using SAR Imagery. The System Employs Deep Learning Models Such As Convolutional Neural Networks (CNN) To Analyze SAR Images And Identify Key Features Such As Road Conditions, Obstacles, And Terrain Patterns. To Enhance Transparency And Trust, Explainable AI Techniques Such As SHAP (SHapley Additive ExPlanations) And GradCAM Are Integrated To Provide Visual Explanations Of Model Decisions. These Explanations Help Military Analysts Understand Why Certain Routes Or Logistics Decisions Are Recommended. The Proposed System Improves Decision-making Accuracy, Reduces Operational Risks, And Ensures Transparency In Critical Military Operations. The Implementation Is Carried Out Using Python And Deep Learning Frameworks, Providing A Scalable And Efficient Solution For Real-time Supply Chain Optimization.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2007-2013

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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