Optimizing Satellite Imagery Object Detection in Challenging Weather Conditions using IoT-Driven Fusion Strategies

IoT devices collect data from remote areas for applications like environmental monitoring and disaster management. Satellite imagery complements this data by providing largescale coverage and context, enhancing decision-making in various domains. Aligning sensor data with satellite imagery resolution to integrate IoT and satellite data for advanced analytics poses a challenge, requiring a balance between local and global detection capabilities in the context of diverse spatiotemporal characteristics. Diverse weather conditions further complicate detection accuracy within IoT frameworks, emphasizing the need for comprehensive data capture and accurate analytics. To address this, we employ weighted fusion techniques that combine multiple detection models like You Only Look Once version (YOLOv7) and Detection TRansformer (DETR) algorithms to achieve objected detection with enhanced accuracy and robustness across different weather conditions and spatial scales. Specifically, this method incorporates the Weighted Fusion with Intersection over Union Matching (WF-IoU), Machine Learning (ML)-based fusion using Random Forest Regression (RFR), AdaBoost Regression (ABR), and XGBoost Regression (XGBR), as well as Deep Learning (DL)based fusion. These techniques integrate various algorithms to optimize object detection performance within IoT environments. Nevertheless, determining optimal Fusion Weights (FWs) and ensuring robustness across scenarios remains a challenge. Furthermore, addressing computational complexities in training and optimizing fusion models is imperative for seamless integration into IoT system. To validate our proposals, we conducted detection tasks across eight weather conditions, simulating them with a Satellite Cloud Generator (SCG). From that, we found that fusion method and its sub-approaches outperform the individual algorithms, enhancing object detection in integration with IoT applications, and thereby improving decision-making and resource management in complex scenarios.