Dr Jaime Zabalza

Research Fellow

Electronic and Electrical Engineering

Personal statement

Jaime joined the Centre for Signal and Image Processing (CeSIP) to undertake a MSc in Electronic and Electrical Engineering in 2011 and a PhD in Hyperspectral Remote Sensing (2012-2015).

He has been a Research Associate since April 2018. Previously, he took a number of research positions at the Universitat Jaume I, Universitat Politecnica de Valencia and the Energy Technological Institute in Spain.


Has expertise in:

    Digital Signal Processing

    Artificial Intelligence

    Machine Learning

    Software Engineering


    MATLAB, C/C#/C++

Prizes and awards

IEEE Brain Data Bank Challenges and Competitions
IEEE Brain Data Bank Challenges and Competitions
IET V&I Best PhD Thesis Award (only one in UK per year)

More prizes and awards


PhD in Electronic and Electrical Engineering (2012-2015)

MSc in Electronic and Electrical Engineering (2011-2012)

MAS in Electrical Technology (2008-2010)

MEng in Industrial Engineering (2000-2005)

PGCert in Researcher Professional Development (2015)

Spanish Chartered Industrial Engineer (COIICV - 5288)


3D expansion of SRCNN for spatial enhancement of hyperspectral remote sensing images
Aburaed Nour, Alkhatib Mohammed Q, Marshall Stephen, Zabalza Jaime, Al Ahmad Hussain
2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021 2021 4th International Conference on Signal Processing and Information Security, ICSPIS 2021, pp. 9-12 (2021)
Folded LDA : extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing
Fabiyi Samson Damilola, Murray Paul, Zabalza Jaime, Ren Jinchang
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol 14, pp. 12312-12331 (2021)
PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification
Yan Yijun, Ren Jinchang, Liu Qiaoyuan, Zhao Huimin, Sun Haijiang, Zabalza Jaime
IEEE Geoscience and Remote Sensing Letters (2021)
SpaSSA : superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image
Sun Genyun, Fu Hang, Ren Jinchang, Zhang Aizhu, Zabalza Jaime, Jia Xiuping, Zhao Huimin
IEEE Transactions on Cybernetics (2021)
A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19
Ren Jinchang, Yan Yijun, Zhao Huimin, Ma Ping, Zabalza Jaime, Hussain Zain, Luo Shaoming, Dai Qingyun, Zhao Sophia, Sheikh Aziz, Hussain Amir
IEEE Journal of Biomedical and Health Informatics Vol 24, pp. 3551-3563 (2020)
2D-SSA based multiscale feature fusion for feature extraction and data classification in hyperspectral imagery
Fu Hang, Sun Genyun, Ren Jinchang, Zabalza Jaime, Zhang Aizhu, Yao Yanjuan
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium , pp. 76-79 (2020)

More publications


Jaime looks after Small Group Tutorials, providing additional academic and general support to first-year students. He has also taught a course: "Signals and Systems" in Jilin, China.

Research interests

Signal and image processing. machine learning, artificial intelligence, data mining, deep learning, big data, hyperspectral imaging. remote sensing, computer vision 2D/3D, robotics, object detection and tracking, non-destructive inspection, condition monitoring, image stitching, smart manufacturing, human-computer interaction.

Professional activities

Remote Sensing (Journal)
Peer reviewer
MPhil Convener
Remote Sensing (Journal)
Guest editor
Beijing University of Technology
Visiting researcher
Northeast Electric Power University
Visiting researcher
Demos on robotically enabled sensing and novel robot control for dynamic environments

More professional activities


NNL Gamechangers GC_142 Phase 5 (Linked to 191761) Image Processing for Enhanced Visualisation
Murray, Paul (Principal Investigator) Marshall, Stephen (Co-investigator) West, Graeme (Co-investigator) Zabalza, Jaime (Co-investigator)
18-Jan-2021 - 31-Jan-2022
Remote Inspection of SNM cans GC_253
Murray, Paul (Principal Investigator) MacLeod, Charles Norman (Co-investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) Zabalza, Jaime (Co-investigator)
01-Jan-2020 - 31-Jan-2020
Image Processing for Enhanced Visual Inspection of Nuclear Waste Packages GC142 Phase 2
Murray, Paul (Principal Investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) West, Graeme (Co-investigator) Zabalza, Jaime (Research Co-investigator)
01-Jan-2020 - 30-Jan-2021
Oilfield data processing
Murray, Paul (Principal Investigator) Campbell, Andrew John (Co-investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) Zabalza, Jaime (Co-investigator)
22-Jan-2019 - 31-Jan-2019
Flexible and Intelligent Path Planning and Control of Industrial Robots towards Autonomous Hot Forging in the Digital Manufacturing Age
Yan, Yijun (Researcher) Mineo, Carmelo (Co-investigator) Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Zabalza, Jaime (Researcher) Fei, Zixiang (Researcher) Wong, Cuebong (Researcher)
The proposed project aims to enhance the autonomous manufacturing capability of UK industry in metal forming and forging. The project brings together two departments of the University of Strathclyde, namely DMEM and EEE. It augments this knowledge with the experts of Strathclyde’s global strategic partner Nanyang Technology University (NTU, Singapore). With Industry 4.0 being currently widely acknowledged as a key driver of industrial advancement, a strong technologic shift has become apparent within industry to move towards both, more intelligence and more autonomy. Currently, hot forging and forming has benefitted only little from this shift beyond traditional automation. There is a vast opportunity to systematically transform the inherently challenging technologies, namely forming and forging into truly smart and flexible manufacturing systems.
The AFRC offers an outstanding practical background for the applied transformation of Industry 4.0 theories. This project aims at delivering practical demonstrators at TRL 6 through implementing advanced knowledge into intelligent robot behaviour and simulation environments for robotic manipulation and flexible automation into the hot forging area considering the “living” and “dirty” environment of such industries, which requires the consideration of humans, hazardous, dynamic, hot and noisy conditions which did not experience much smart automation yet.
01-Jan-2017 - 31-Jan-2018

More projects


Electronic and Electrical Engineering
Royal College Building

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