High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems. (28th August 2021) - Kim, Jae‐In; Chi, Junhwa; Masjedi, Ali; Flatt, John Evan; Crawford, Melba M.; Habib, Ayman F.; Lee, Joohan; Kim, Hyun‐Cheol
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. Abstract : Hyperspectral remote sensing has been developed to detect individual absorption features related to specific chemical bonds in soils, liquids or gases; however, because UAV‐based pushbroom hyperspectral sensor technologies are relatively new, no public datasets are currently available. To address this gap, we acquired hyperspectral imagery over a permafrost area, and provided two hyperspectral datasets from GNSS/INS‐assisted co‐aligned pushbroom hyperspectral scanners on a drone. Our datasets are expected to provide a new perspective for improving UAV‐based hyperspectral data processing and analysis algorithms.