High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems

• Published in: Geoscience Data Journal
• Main Authors: Jae‐In Kim, Junhwa Chi, Ali Masjedi, John Evan Flatt, Melba M. Crawford, Ayman F. Habib, Joohan Lee, Hyun‐Cheol Kim
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: geometric correction; hyperspectral; permafrost; radiometric correction; unmanned aerial vehicle; Meteorology. Climatology; QC851-999; Geology; QE1-996.5
• Online Access: https://doi.org/10.1002/gdj3.133; https://doaj.org/article/cee4c7e1d0c94bba97c827a79d29bda7

Abstract Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various applications and algorithms. However, hyperspectral data are usually limited by their narrow, highly correlated and contiguous spectral bands in both processing and analysis. Moreover, the resolution of available hyperspectral datasets is not sufficiently high for the identification of small objects. Nevertheless, with the rapidly advancing technology, hyperspectral imaging systems can now be mounted on small aerial vehicles for detecting small objects at low altitude. To properly handle these high spectral and spatial resolution data, new or redesigned data processing or analysis pipelines must be developed, but such datasets are limited. In this study, we describe two hyperspectral datasets acquired by a drone and evaluate their radiometric and geometric quality. Based on appropriate data acquisition and processing approaches, our datasets are expected to be useful as testbeds for new algorithms and applications.