Intelligent analytics for hyperspectral image dimension reduction

This research project focuses on improving the analysis of hyperspectral images, which capture images with many narrow spectral bands. Hyperspectral images have many challenges due to their high dimensionality and data redundancy. To overcome these challenges, the researchers aim to develop an advanced intelligent analytics framework for hyperspectral dimension reduction (HDR) using disentangled representation techniques. This approach will extract low-dimensional features that contain the most important information in a compact and interpretable manner. The researchers will investigate and design a new HDR framework that considers spatial-spectral heterogeneity in HSI using advanced deep neural network architectures and spectral models. They will also adapt and extend the HDR methods to improve various HSI processing tasks such as denoising, visualization, and classification. The expected outcome is a practical software system for enhanced HSI interpretation in real-world applications.

Faculty Supervisor:

Linlin Xu

Student:

Partner:

National University of Kyiv-Mohyla Academy

Discipline:

Computer science

Sector:

Artificial Intelligence; Environmental Science and Technology; Agriculture and Food

University:

University of Waterloo

Program: