Recent Advances in Spectral Unmixing for Hyperspectral Imagery
Hyperspectral imaging (HSI) systems collect image data simultaneously in hundreds of narrow, adjacent spectral bands. Hyperspectral remote sensing is used in a wide array of applications such as agriculture, mineralogy, surveillance, etc.
In HSI, the observed radiation represented from a single pixel rarely comes from a pure material. However, the high spectral resolution of the HSI spectrometer enables the detection, identification and classification of subpixel objects and their contribution to the measured spectra. The distinct materials associated with the surface are called endmembers, and the fractions in which they appear in a pixel are called abundances. The unmixing problem refers to finding the number of endmembers, their spectral signatures, and their abundances from a given hyperspectral image.
Speaker
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Dr. Mohammed Q. AlkhatibAssistant Professor, College of Engineering & IT, University of DubaiDr. Mohammed Alkhatib earned his Bachelor in Telecommunications Engineering from Yarmouk University in 2008. He received his Masters’ and PhD degrees in Electrical and Computer Engineering from The University of Texas at El Paso in 2011 and 2018, consecutively.
In 2021, Dr. Alkhatib joined the College of Engineering and IT (CEIT) in University of Dubai as an Assistant professor. Prior to CEIT, he served as an instructor in the Academic Support Department in Abu Dhabi Polytechnic.
Dr. Alkhatib research interests are spectral unmixing for hyperspectral images, HSI classification, and HSI super-resolution.
He presented his research at international Journals and conference meetings including the 2019 Multidisciplinary Digital Publishing Institute (MDPI), Proceedings of the IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) and the Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS).