Super GeoAI Technology Inc. (SGA) is excited to announce that our researchers, Dr. Riyaaz Uddien Shaik and Dr. Weiping Zeng, have published a groundbreaking paper titled “Quantum-Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers” in the highly esteemed journal Remote Sensing. This publication underscores our commitment to advancing the field of geospatial artificial intelligence (GeoAI) and contributing valuable insights to the academic community.
The paper introduces an innovative method for hyperspectral imagery analysis, leveraging quantum-based pseudo-labelling to enhance the performance of machine learning classifiers. This semi-supervised learning approach offers a simple yet efficient solution for processing hyperspectral data, which has significant applications in various fields, including agriculture, environmental monitoring, and urban planning.
By integrating quantum computing principles with machine learning, Dr. Shaik and Dr. Zeng’s research provides a novel framework that improves the accuracy and efficiency of hyperspectral image classification. Their work demonstrates the potential of combining advanced technologies to address complex geospatial challenges and underscores SGA’s dedication to pushing the boundaries of GeoAI research.
We extend our heartfelt congratulations to Dr. Riyaaz Uddien Shaik and Dr. Weiping Zeng for their outstanding contribution to the field. Their research not only advances our understanding of hyperspectral imagery but also exemplifies the innovative spirit of SGA.
We invite you to read the full paper in Remote Sensing:
Stay tuned for more updates on our research projects and other exciting developments at SGA.