Multispectral and Hyperspectral Imaging for Next-Generation Sensing Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 5 October 2025 | Viewed by 581
Special Issue Editors
Interests: hyperspectral image processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: infrared imaging; artificial intelligence; image processing; embedded system
Special Issues, Collections and Topics in MDPI journals
Interests: imaging processing; infrared and hyperspectral imaging; small target detection
Special Issues, Collections and Topics in MDPI journals
Interests: infrared and spectral signal processing technologies and theories
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral imaging has emerged as a crucial tool in remote sensing geology, providing essential support for applications such as land and mineral resource management, geological disaster early warning systems, and military functions, including air defenses and camouflage detection. The European Union's white paper, "100 radical innovation breakthroughs for the future," identifies hyperspectral imaging as a disruptive technology poised to significantly impact the global economy. Despite its promise, achieving high spatial, temporal, and spectral resolution simultaneously—termed the "three high resolutions"—remains a significant bottleneck in both military and civilian applications due to inherent trade-offs among these dimensions. This Special Issue focuses on the use of innovative systems for high-resolution hyperspectral imaging, alongside advanced methodologies such as super-resolution techniques, spectral unmixing, anomaly detection, and refined classification processes. Furthermore, given the widespread use of deep learning and the scarcity of training data in remote sensing, this Special Issue will explore advancements in hyperspectral data generation. This research seeks to mitigate the challenge of "data scarcity," facilitating a shift from basic classification to more sophisticated identification of hyperspectral targets. By supporting models with increased parameter scale and complexity, we will provide the theoretical and methodological foundations necessary to advance applications into the era of large-scale remote sensing models.
Dr. Xiaoguang Mei
Dr. Jun Huang
Dr. Fan Fan
Dr. Hao Li
Guest Editors
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Keywords
- hyperspectral imaging
- remote sensing geology
- hyperspectral applications
- three high-resolution imaging
- data generation
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