HI-DSR: Hyperspectral Images enhancement via Deep Sparse Representation model

The current non-destructive and fast method of hyperspectral imaging technology are used for different application from remote sensing to medical imaging and food processing. Due to the nature of acquired data which are massive as well as physical consideration depend on the type of application, the careful, fast and accurate data analysis is mandatory to accelerate the usage of HSI technology. To cope with the type of HSI data in which the sparsity assumption is applicable, this project aims to address the HSI data representation though the sparse problem under deep learning approach. Eventually, the research project will provide possible unique feature extraction spatially/spectrally to give more accurate/realistic results but provable in the sense of sparsity.

Faculty Supervisor:

Saeed Gazor

Student:

Ahmed Faid Alrashidy;Mohammadkia Zamiri-Jafarian

Partner:

MatrixSpec Solutions

Discipline:

Engineering - computer / electrical

Sector:

Agriculture

University:

Queen's University