Hyperspectral Remote Sensing for Complex Scene Understanding and Practical Applications

Dear Colleagues,

Hyperspectral remote sensing provides detailed spectral and spatial information that enables the fine-grained observation and analysis of the Earth’s surface. It has shown strong potential in a wide range of fields, including in environmental monitoring, precision agriculture, forestry, disaster response, and resource exploration. In recent years, advances in imaging systems, big data processing, and artificial intelligence have substantially improved the capability of hyperspectral imagery in terms of scene understanding and feature extraction. However, complex real-world scenes still present major challenges, such as spectral variability, mixed pixels, noise interference, background heterogeneity, and limited labeled data. These issues make robust understanding and the practical application of hyperspectral data far from easy, and call for further research on robust and efficient methods for hyperspectral data interpretation in complex scenes.

This Special Issue aims to present recent advances in the theories, methods, datasets, and applications that improve the understanding, interpretation, and deployment of hyperspectral remote sensing in complex scenes. Topics of interest include, but are not limited to, the following:

• Spectral unmixing and subpixel information extraction;
• Hyperspectral image restoration and super-resolution;
• Image-level and pixel-level classification;
• Object-level and pixel-level change detection and target detection;
• Multimodal and multitemporal hyperspectral data analysis;
• Hyperspectral benchmark datasets;
• Real-world applications in agriculture, forestry, ecology, and related domains.

Dr. Degang Wang
Dr. Lina Zhuang
Dr. Lifeng Wang
Dr. Marina Ljubenovic
Guest Editors

Manuscript Submission Information

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