Advances in Machine Learning and Deep Learning for Hyperspectral Image Classification 

About the Special Session

Hyperspectral image classification is a rapidly evolving field with significant relevance to industrial, environmental, and security applications. Hyperspectral imaging captures high-dimensional data across hundreds of spectral bands, enabling detailed analysis of materials, chemicals, and environmental features. However, extracting meaningful information from hyperspectral data is highly challenging due to the high dimensionality, unbalanced class distributions, limited labeled samples, spectral variability, and complex interactions with substrates or backgrounds.

The objective of this special session is to bring together researchers and practitioners in machine learning, deep learning, and remote sensing to share novel computational approaches for remote sensing and hyperspectral imaging. The session will focus on recent advances that enhance classification accuracy, robustness, and generalization in real-world scenarios, where traditional methods often fail. Specifically, it aims to highlight innovative solutions that leverage AI to process large-scale hyperspectral datasets efficiently, overcome class imbalance, handle unlabeled or weakly labeled data, and extract meaningful spectral and spatial features.

Topics of interest include, but are not limited to:

• Semantic Segmentation of Remote Sensing Images

• SAR Image Segmentation

• Hyperspectral Image Classification

• Graph Learning

• Machine Learning and Deep Learning

• Applications of Remote Sensing and Hyperspectral Imaging

Key Information

• All accepted papers will be included in the WCCI 2026 proceedings, which will be published in the IEEE Xplore Digital Library.

• The submission portal is: https://ssl.linklings.net/conferences/WCCI/. Please select “IJCNN SS02: Advances in Machine Learning and Deep Learning for Hyperspectral Image Classification.”

• Submission deadline: January 31, 2026 (no extensions will be granted).

Primary Contact

Nian Ashlee Zhang, Ph.D.

Professor of Electrical and Computer Engineering

Department of Electrical and Computer Engineering

School of Engineering and Applied Sciences (SEAS)

University of the District of Columbia

4200 Connecticut Avenue, NW, Washington, D.C. 20008 USA

Office: +1 (202) 274-6615

Email: nzhang@udc.edu

Organizers

Nian Ashlee Zhang, Ph.D. 

Professor of Electrical and Computer Engineering

Department of Electrical and Computer Engineering 

School of Engineering and Applied Sciences (SEAS) 

University of the District of Columbia

4200 Connecticut Avenue, NW, Washington, D.C. 20008 USA

Office: +1 (202) 274-6615

Email: nzhang@udc.edu

Webpage: https://www.udc.edu/directory/profiles/seas/nian-zhang

Man-Fai Leung, Ph.D.

Senior Lecturer

School of Computing and Information Science

Faculty of Science and Engineering

Anglia Ruskin University

Cambridge, U.K.

Email: man-fai.leung@aru.ac.uk

Webpage: https://www.aru.ac.uk/people/man-fai-leung

https://scholar.google.co.uk/citations?hl=en&user=auIXUW8AAAAJ

Feng Xia, Ph.D.

Professor of Data Science & AI

School of Computing Technologies

RMIT University

Melbourne, VIC 3000, Australia

Email: f.xia@ieee.org

Webpage: https://www.xia.ai/

Donald C. Wunsch II, Ph.D., MBA

Mary Finley Missouri Distinguished Professor

Director of Kummer Institute Center for AI and Autonomous Systems

Missouri University of Science and Technology

Rolla, MO 65409 USA

Email: dwunsch@mst.edu

Webpage: https://sites.mst.edu/wunschkicaias/

https://caias.mst.edu/

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