Satellite Workshop at IEEE ICIP 2025
Hyperspectral imaging has undergone remarkable advancements in recent years, shifting from labor-intensive and time-consuming processing methods to efficient, real-time analysis techniques. This workshop focuses on leveraging deep learning to tackle the unique challenges posed by hyperspectral imaging, including its inherent spectral complexity, high dimensionality, and the critical task of preserving spectral band integrity—an aspect often overlooked in conventional methods. The workshop will explore intelligent algorithms for automated data interpretation, advanced data fusion techniques for multi-source integration, and strategies to enhance spectral continuity in model outputs. Topics such as transfer learning, automated image classification, and segmentation will also be highlighted, showcasing their role in advancing hyperspectral imaging capabilities. This workshop aims to foster innovation and collaboration by bringing together leading researchers and practitioners advancing in hyperspectral imaging and applied AI applications across diverse sectors such as healthcare, environmental monitoring, agriculture, public safety, forensics sciences, and defense.
Areas of Focus:
• Intelligent Algorithms for Automated Hyperspectral Data and Spectral Analysis
• Optimizing Deep Learning Architectures for Multispectral and Hyperspectral Imaging
• Preservation of Spectral Continuity in Deep Learning Models
• Multi-Source Data Fusion for Hyperspectral Analysis
• Transfer Learning for Hyperspectral and Multispectral Image Analysis
• Automated Classification and Segmentation of Hyperspectral and Multispectral Imagery