A deep learning information detection and recognition cloud-based system architecture for multi-source remote sensing images

Traditional methods for remote sensing image analysis often struggle with the complexity and variability of large-scale datasets. However, the advent of deep learning, particularly Convolutional Neural Networks (CNNs), has significantly enhanced the capability to automatically extract features and improve detection accuracy. This paper presents a deep learning-based system architecture for object detection and recognition in multi-source remote sensing imagery. The proposed architecture integrates advanced deep learning algorithms with robust cloud computing resources, enabling efficient processing and analysis of high-resolution satellite imagery. The system incorporates a multi-modal training strategy to enhance model adaptability across diverse datasets and imaging conditions. Additionally, it features an evaluation framework that assesses performance using key metrics such as precision, False Negative Rate (FNR), False Positive Rate (FPR), recall and F1-Score. Experimental results demonstrate the system's high accuracy and robustness in detecting various objects, including airplanes, ships, oil tanks, and bridges, using both optical and Synthetic Aperture Radar (SAR) imagery. Future work will focus on further optimizing the system's flexibility and integrating emerging technologies to maintain its cutting-edge performance in the rapidly evolving fields of remote sensing and artificial intelligence.