High-resolution hyperspectral images of Council, Alaska 2019

• Main Authors: Junhwa Chi, Jae-In Kim, Ali Masjedi, John Evan Flatt, Melba M. Crawford, Ayman F. Habib, Joohan Lee, Hyun-Cheol Kim
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2021
• Subjects: Remote sensing; Hyperspectral; UAV; Permafrost; Council, Alaska; permafrost; Alaska
• Online Access: https://doi.org/10.18739/A2416T10D

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. However, such datasets are currently not publicly available. Therefore, we provide two hyperspectral datasets from GNSS/INS-assisted co-aligned pushbroom hyperspectral scanners on a drone. The hyperspectral datasets consist of raw/post-processed hyperspectral data, raw/post-processed Global Navigation System Satellite/Inertial Measurement Unit (GNSS/IMU) data, and digital surface models, and were radiometrically and geometrically evaluated. These datasets are expected to aid the improvement of UAV-based hyperspectral data processing and analysis algorithms.