Algorithms in Hyperspectral Data Analysis

Dear Colleagues,

At present, thanks to the continuous evolution of sensor technologies for hyperspectral imaging, there is a high demand for the design of algorithms, techniques, and methods for the analysis of hyperspectral images. Hyperspectral images are a rich source of information, since they contain precious spatial and spectral contents, differently than traditional images.

In several research and real-life fields, hyperspectral images play an important role (e.g., agriculture, counter-terrorism, archaeology, forensic applications, environment monitoring, medicine). Furthermore, thanks to technology advances, it is now also possible to integrate the hyperspectral sensors in platforms where it was previously not possible (e.g., unmanned aerial vehicle platforms).

Therefore, in the future, there will be new scenarios and fields of application in which hyperspectral data will be involved in order to bring their precious contributions.

The aim of this Special Issue is therefore to welcome all relevant and recent advances from the scientific community, regarding research and studies related to the algorithms in hyperspectral data analysis.

The topics include but are not limited to the following areas:

• Hyperspectral data classification;
• Techniques and methods for data fusion;
• Restoration;
• Algorithms for hyperspectral unmixing;
• Distributed and parallel processing;
• Hyperspectral target detection;
• Cloud platform for hyperspectral data analysis;
• Supervised and semisupervised classification;
• Lossless and lossy compression;
• Real-time processing;
• Analysis based on machine learning;
• Data preprocessing

Dr. Raffaele Pizzolante
Guest Editor

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