Multimodal Remote Sensing Data Fusion for Classification: Algorithms and Applications provides a foundation for Earth observation data fusion using multimodal remote sensing, offering cutting-edge algorithms and practical applications. Through detailed analysis and case studies, the book equips readers with the knowledge and tools to utilize multimodal remote sensing data fusion to better understand Earth's dynamic processes and promote sustainable solutions in the classification and mapping of land cover and land use, and monitoring environmental change. Multimodal Remote Sensing Data Fusion for Classification: Algorithms and Applications provides Masters and Doctorate students, scientists and professionals in remote sensing, geography and Earth sciences with a foundation in integrating and analyzing multimodal remote sensing data.

• Provides a holistic overview of Multimodal Remote Sensing, from data acquisition and preprocessing to fusion techniques, to analysis methodologies and diverse applications
• Includes real-world case studies and examples, showcasing the application of multimodal remote sensing in various fields
• Emphasizes future perspectives and emerging technologies, providing readers with forward-thinking applications and their potential impact on the field