Using visible spectral information to predict long-wave infrared spectral emissivity: A case study over the sokolov area of the czech republic with an airborne hyperspectral scanner sensor

Simon Adar, Yoel Shkolnisky, Gila Notesco, Eyal Ben-Dor

Research output: Contribution to journalArticlepeer-review

Abstract

Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor's missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR) spectral region using the visible (VIS) spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS) sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic.

Original languageEnglish
Pages (from-to)5757-5782
Number of pages26
JournalRemote Sensing
Volume5
Issue number11
DOIs
StatePublished - Nov 2013

Keywords

  • Emissivity prediction
  • Imputation
  • K nearest neighbors
  • Missing data
  • Multisensor analysis
  • Sensor-to-sensor (SENTOS) prediction

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