Satellite Workshop Series at IEEE ICIP
Hyperspectral imaging has seen remarkable advancements in recent years, moving from labor intensive and time-consuming processing methods to efficient real time analysis techniques. This workshop focuses on leveraging deep learning and foundation models to address the unique challenges of hyperspectral imaging, including spectral complexity, high dimensionality, and the critical task of preserving spectral band integrity, an aspect often overlooked in conventional approaches. The workshop will explore intelligent algorithms for automated data interpretation, advanced data fusion techniques for multi-source integration, and strategies to maintain spectral continuity in model outputs. Topics such as transfer learning, automated image classification, and segmentation will be highlighted, demonstrating their role in enhancing hyperspectral imaging capabilities. This workshop aims to foster innovation and collaboration by bringing together leading researchers and practitioners in hyperspectral imaging and applied artificial intelligence across sectors including healthcare, environmental monitoring, agriculture, public safety, 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
Conference-Specific Information:
Information on the upcoming conference can be found in the menu above. Information on past conference(s) can be found under the archive tab. Accepted and presented conference papers, once published, can be found within their respective year’s workshop program page.