Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies

Dear Colleagues,

Coastal ocean environments are profoundly important to global ecology. The effective monitoring and sustainable management of these vital environments requires comprehensive multidisciplinary understanding. Hyperspectral remote sensing provides one of the most effective tools for understanding coastal ecosystems and their connections to the ocean and land interfaces. Currently, hyperspectral data are being used in many applications related to coastal areas, such as the characterization of phytoplankton, measurement of water quality, analysis of the incidence of algal blooms, and coastal fisheries. In this Special Issue, advanced techniques for the analysis of coastal ocean environments using hyperspectral remote sensing data will be presented.

This Special Issue aims to collect articles addressing new developments and methodologies, best practices and applications of hyperspectral remote sensing in coastal environments. We invite you to submit your most recent advancements on all relevant topics, including but not limited to the following:

1. Advanced data pre-processing methods for coastal hyperspectral images (e.g., dimensionality reduction, band selection, image denoising, etc.).
2. Fusion of hyperspectral and other remote sensing data (e.g., Lidar, multispectral imagery, SAR, and Pan images) for coastal analyses.
3. Diagnostic spectrum characteristic extraction and feature analysis of coastal ground objects (e.g., wetland, vegetation, and water).
4. Training sample selection and optimization techniques for hyperspectral data in coastal areas.
5. Fine identification of coastal ground objects using hyperspectral image processing (e.g., classification, spectral unmixing, and target detection).
6. Temporal change detection and analysis of coastal hyperspectral images.
7. Improved algorithms in big coastal hyperspectral data processing, such as deep learning and transfer learning.

Dr. Weiwei Sun
Dr. Ruiliang Pu
Dr. Dengsheng Lu
Dr. Jiangtao Peng
Dr. Jia Wan
Guest Editors

Manuscript Submission Information

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