Machine Learning-Based Handling of Geospatial Big Data From Hyperspectral Sensors for Urban Area Characterization

Abstract

Investigating urban processes requires detailed built-up surface material composition information, which is possible through constant monitoring by recently launched spaceborne hyperspectral sensors. However, they are plagued by medium spatial resolutions and mixed pixels. Super-resolution (SR) and spectral unmixing can address the former and latter, respectively. Machine learning, due to its predictive capability, has become indispensable for studying these datasets. Hence, remote sensing researchers, need to understand machine learning algorithms thoroughly. Herein, works on machine learning application for urban hyperspectral sensing have been thoroughly analyzed. A functional SR classification scheme has also been introduced. Super-resolved product quality metric evaluation, open-source urban spectral libraries, benchmark urban scenes meant for training and testing new SR, or unmixing algorithms have been briefly reviewed. Finally, difficulties with hyperspectral image processing based on machine learning have been raised, along with future research directions.