Remote Sens. 2013, 5(11), 5757-5782; doi:10.3390/rs5115757
Article

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

1,* email, 2email, 3email and 3email
Received: 10 September 2013; in revised form: 16 October 2013 / Accepted: 16 October 2013 / Published: 6 November 2013
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: missing data; imputation; k nearest neighbors; multisensor analysis; emissivity prediction; sensor-to-sensor (SENTOS) prediction
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MDPI and ACS Style

Adar, S.; Shkolnisky, Y.; Notesco, G.; Ben-Dor, E. 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. Remote Sens. 2013, 5, 5757-5782.

AMA Style

Adar S, Shkolnisky Y, Notesco G, Ben-Dor E. 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. Remote Sensing. 2013; 5(11):5757-5782.

Chicago/Turabian Style

Adar, Simon; Shkolnisky, Yoel; Notesco, Gila; Ben-Dor, Eyal. 2013. "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." Remote Sens. 5, no. 11: 5757-5782.


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