Recent Advances in Multi- and Hyperspectral Image Analysis

Dear Colleagues,

Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from the detailed information available in up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been designing a range of image-processing- and machine-learning-powered approaches toward efficient processing of such data. To this end, multi/hyperspectral analysis has bloomed and become an exciting research area which can enable faster adoption of this technology in practice, also when deployed in hardware-constrained and extreme execution environments, e.g., on board of imaging satellites.

The aim of this Special Issue is to gather and present recent advances in multi- and hyperspectral image analysis. The core themes of this topic cover all steps of the data processing pipeline, from its acquisition to final analysis and understanding. These themes include but are not limited to:

• Pre/post-processing of multi/hyperspectral images;
• Band selection from multi/hyperspectral images;
• Feature extraction and learning from multi/hyperspectral images;
• Data fusion of high-dimensional data;
• Spectral and spatial super-resolution;
• Spectral unmixing;
• Deep learning-powered algorithms for multi/hyperspectral data analysis;
• Classification and segmentation of multi/hyperspectral images;
• Multitemporal and multisensor analysis;
• Event detection and tracking;
• Prediction from multi/hyperspectral data;
• Deployment of machine/deep learning-powered techniques for multi/hyperspectral data analysis in hardware-constrained environments;
• Robustness of deep learning-powered techniques for multi/hyperspectral data analysis;
• Concept drift in multi/hyperspectral data analysis.

Dr. Jakub Nalepa
Guest Editor

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