Trends and Prospects in Hyperspectral Image Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 447
Special Issue Editors
Interests: big data classification; meta-learning; class imbalance; time series; ensembles, neural networks
Special Issues, Collections and Topics in MDPI journals
Interests: big data; deep learning; sampling methods; neural networks
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition; machine learning; data mining; data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral imaging is an advanced technique that combines imaging and spectroscopy to capture and analyze a wide range of spectral information. It has found applications in various fields, including remote sensing, agriculture, environmental monitoring, medical diagnostics, and industrial inspection. Here, we present some of the trends and prospects in hyperspectral imaging:
Increased availability of hyperspectral sensors: Over the years, there has been a significant improvement in the availability and affordability of hyperspectral sensors. This has resulted in wider adoption and integration of hyperspectral imaging technology into various industries.
Advancements in sensor technology: Ongoing advancements in sensor technology have led to the development of smaller, lighter, and more efficient hyperspectral imaging systems. This has allowed for easier integration into unmanned aerial vehicles (UAVs), satellites, and handheld devices, expanding the possibilities for data collection and analysis.
Integration with machine learning and AI: Hyperspectral imaging generates vast amounts of data that can be challenging to interpret and analyze manually. However, the integration of machine learning and artificial intelligence techniques has shown great promise in extracting valuable insights and patterns from hyperspectral data. These approaches can aid in automated image classification, target detection, and anomaly identification.
Prof. Dr. Vicente García
Prof. Dr. Roberto Alejo Eleuterio
Prof. Dr. José Salvador Sánchez Garreta
Guest Editors
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Keywords
- machine learning
- artificial intelligence (AI)
- image classification
- target detection
- anomaly identification
- data compression
- feature extraction
- data fusion
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