An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification




edge preserving features, hyperspectral image classification, minimum noise fraction, principal component analyses, support vector machine


Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes. In this paper, we conduct a systematic study to examine the effects of noise in HSI data cubes on classification methods. We compare ten existing methods for HSI classification when Gaussian white noise (GWN) and shot noise are present in the HSI data cubes. We have figured out which method is the most robust to GWN and shot noise respectively by experimenting on three widely used HSI data cubes. We have also measured the CPU computational time of every method compared in this paper for HSI classification.

Author Biography

Guangyi Chen, Concordia University

Guang Yi Chen holds a B.Sc. in Applied Mathematics, an M.Sc. in Computing Mathematics, an M.Sc. in Computer Science, and a Ph.D. in Computer Science. During his graduate and postdoctoral studies in Canada, he was awarded many prestigious fellowships. He has published over sixty-five scientific journal papers in his fields and holds two granted USA patents in image processing. He is currently affiliated to the Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada. He is the world's top 2% scientist ranked by Stanford University. His research interests include pattern recognition, image processing, machine learning, artificial intelligence, remote sensing, and scientific computing.


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How to Cite

Chen, G., Krzyzak, A., & Qian, S.- en. (2024). An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification . Image Analysis and Stereology, 43(2), 195–201.



Original Research Paper