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RSS FeedsRemote Sensing, Vol. 11, Pages 2428: Radiometric and Atmospheric Corrections of Multispectral ?MCA Camera for UAV Spectroscopy (Remote Sensing)

 
 

19 october 2019 13:00:02

 
Remote Sensing, Vol. 11, Pages 2428: Radiometric and Atmospheric Corrections of Multispectral ?MCA Camera for UAV Spectroscopy (Remote Sensing)
 


This study presents a complex empirical image-based radiometric calibration method for a Tetracam μMCA multispectral frame camera. The workflow is based on a laboratory investigation of the camera’s radiometric properties combined with vicarious atmospheric correction using an empirical line. The effect of the correction is demonstrated on out-of-laboratory field campaign data. The dark signal noise behaviour was investigated based on the exposure time and ambient temperature. The vignette effect coupled with nonuniform quantum efficiency was studied with respect to changing exposure times and illuminations to simulate field campaign conditions. The efficiency of the proposed correction workflow was validated by comparing the reflectance values that were extracted from a fully corrected image and the raw data of the reference spectroscopy measurement using three control targets. The Normalized Root Mean Square Errors (NRMSE) of all separate bands ranged from 0.24 to 2.10%, resulting in a significant improvement of the NRMSE compared to the raw data. The results of a field experiment demonstrated that the proposed correction workflow significantly improves the quality of multispectral imagery. The workflow was designed to be applicable to the out-of-laboratory conditions of UAV imaging campaigns in variable natural conditions and other types of multiarray imaging systems.


 
188 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2429: Spatial Pattern of Soil Erosion Drivers and the Contribution Rate of Human Activities on the Loess Plateau from 2000 to 2015: A Boundary Line from Northeast to Southwest (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2427: Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data (Remote Sensing)
 
 
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