Spectral quality indicators for hyperspectral data

Anna Brook*, Eyal Ben Dor

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

A novel approach to estimating at-sensor hyperspectral (HRS) data quality Q/A of Q/I is proposed. As the HRS sensor's performance may vary in time and space, a method to assess at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and spectrally/radiometrically homogeneous. Since such targets are not commonly found in the field for every mission and their spectral features can sometimes resemble artifacts in the corrected radiance, a new vicarious calibration approach is needed. This paper is based on our new method Supervised Vicarious Calibration (SVC) that uses artificial agricultural black polyethylene nets of various densities as vicarious calibration targets that are set up along the airplane's trajectory (preferably near the airfield). The different densities of the nets combined with any bright background afford full coverage of the sensor's dynamic range. We show that these artificial targets can be used to assess at-sensor radiance data quality within a short time by two suggested indicators named Rad/Ref (at-sensor Radiance divided by ground truth Reflectance) and RRDF (Radiance to Reflectance difference factor). It's enables gaining immediate Q/A of Q/I information on the acquired data, prior to completion of the campaign, which could save on flight hours, effort and resources in the case of a radiometrically miscalibrated sensor. Several case studies are presented using AISA-Dual sensor data taken at different times and locations. We demonstrate the performance of the suggested indicators in both spectral and spatial domains and discuss their limitation.

Keywords

  • AISA-Dual sensor
  • Calibration coefficients
  • Hyperspectral data calibration
  • QA of QI
  • Radiometric uncertainty

Fingerprint

Dive into the research topics of 'Spectral quality indicators for hyperspectral data'. Together they form a unique fingerprint.

Cite this