Intelligent Edge AI System for Satellite Image Analysis and Adaptive Data Transmission

Abstract: Satellite imagery plays an important role in environmental monitoring, disaster management, and land-surface observation. However, processing large volumes of satellite images and transmitting them to ground stations can create challenges due to limited communication bandwidth, processing resources, and onboard energy availability. This paper presents an Intelligent Edge AI System for Satellite Image Analysis and Adaptive Data Transmission that integrates deep learning-based image classification with confidence-based transmission decisions. A Convolutional Neural Network (CNN) is employed to classify satellite images into four categories: Fire, Water, Cloud, and Land. Before classification, images are resized to 128 × 128 pixels, converted to RGB format, and normalized to improve input consistency. Based on the predicted class, confidence score, and simulated battery conditions, a decision engine determines whether an image should be sent, marked for compression, or ignored. The system also incorporates Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the image regions that influence classification results. Its performance can be assessed using accuracy, precision, recall, F1-score, a confusion matrix, and prediction latency. The proposed framework combines image analysis, adaptive decision-making, and simulated energy-aware processing within a unified system. The current implementation provides a prototype for intelligent satellite image handling; further validation using reliable datasets, actual image compression, and real communication hardware is required to quantify its impact on transmission efficiency and energy consumption.

Keywords: Edge Artificial Intelligence, Satellite Image Analysis, Convolutional Neural Network, Adaptive Data Transmission, Energy-Aware Computing, Grad-CAM, Remote Sensing.