Deep Learning in Satellite and Aerial-Based Image Processing

The improvement of Earth Observation (EO) satellite resolutions in recent years, coupled with the increasing requests for higher performance, introduced the need for more advanced detection techniques for satellite image analysis. Modern EO platforms generate vast amounts of data, rich in potentially valuable information but often underutilized, making the adoption of efficient image processing solutions crucial. These information can be essential for accelerating processes such as classification and geo-referencing, ensuring the timely availability of mission products. In this context, the introduction of deep learning techniques, particularly the You Only Look Once (YOLO) algorithm, represents a natural evolution. YOLO is known for its speed, as it analyzes the entire image in a single pass, and for its precision, due to the use of deep convolutional neural networks (CNNs). When applied to satellite images, YOLO has shown promising results, especially for automatic geo-referencing and rapid classification. A first attempt at a comparative analysis between models trained with 60 and 100 epochs, applied to optical Sentinel-2 images targeting Italian lakes under various weather conditions, revealed significant improvements in detection precision and consistency. In particular, the accuracy of boundaries improved as training epochs increased. As the number of epochs grew, the results became more stable, regardless of environmental or lighting conditions, reducing errors and improving overall performance. These advancements suggest that with further development of algorithms and integration of artificial intelligence, the use of satellites and drones in geospatial applications will become increasingly precise and efficient. The use of drone images could further expand datasets, allowing the model to respond with an adaptive approach to specific details or defined elements, such as artificial structures or small areas of interest that satellites may not be able to detect with the same precision, especially due to unfavorable weather conditions. This integrated approach combining satellite and aerial data could further enhance the model’s ability to detect smaller objects or handle more complex environments, increasing the versatility and reliability of automatic detection solutions in realworld contexts.