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

Simon Adar³  Yoel Shkolnisky¹  Gila Notesco² 

[1] Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel; E-Mail:;Department of Geography, Tel Aviv University, Tel Aviv 39040, Israel; E-Mails:;Porter School of Environmental Studies, Tel Aviv University, Tel Aviv 69978, Israel

关键词: missing data;    imputation;    k nearest neighbors;    multisensor analysis;    emissivity prediction;    sensor-to-sensor (SENTOS) prediction;   

DOI  :  10.3390/rs5115757

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland