The text begins with a detailed introduction to the core principles of remote sensing, offering readers foundational knowledge before delving into the growing role of artificial intelligence. It systematically explores how artificial intelligence-driven methods are transforming the way satellite imagery and other forms of remote sensing data are processed, analyzed, and interpreted across various domains. This book:Includes in-depth analysis of the latest research findings and real-world case studies highlighting successful applications of artificial intelligence in remote sensing. Addresses emerging trends like explainable artificial intelligence and federated learning, ensuring that the readers understand the future of artificial intelligence-driven remote sensing. Presents advanced machine learning and deep learning methods for spectral and spatial feature extraction in remote sensing. Explains artificial intelligence for unmanned aerial vehicle (UAV) and hyperspectral remote sensing. Explores big data analytics for remote sensing, and quantum machine learning for high dimensional remote sensing data. It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, environmental engineering, and information technology.
