Insights from this study could be applied to improve ship detection technologies beyond SAR imaging by enhancing detection capabilities under varying environmental conditions:
Multi-Sensor Fusion: Integrating information from multiple sensors such as AIS (Automatic Identification System) data with SAR imagery could enhance ship detection accuracy and reduce false alarms by combining complementary sources of information.
2Adaptive Thresholding Techniques: Leveraging non-parametric thresholding techniques like Wilcoxon's method could improve ship detection algorithms' adaptability across different sea states or clutter backgrounds.
3Machine Learning Integration: Incorporating machine learning algorithms trained on diverse datasets could further refine ship classification tasks within SAR images while accounting for complex features such as vessel size variations or orientation changes.
4Real-Time Decision Support Systems: Developing real-time decision support systems that leverage insights from non-parametric detections could enable quicker response times during maritime surveillance operations by reducing manual intervention requirements while maintaining high accuracy levels.