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Proceedings Paper

A case study of comparing radiometrically calibrated reflectance of an image mosaic from unmanned aerial system with that of a single image from manned aircraft over a same area
Author(s): Yeyin Shi; J. Alex Thomasson; Chenghai Yang; Dale Cope; Chao Sima
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Paper Abstract

Though sharing with many commonalities, one of the major differences between conventional high-altitude airborne remote sensing and low-altitude unmanned aerial system (UAS) based remote sensing is that the latter one has much smaller ground footprint for each image shot. To cover the same area on the ground, it requires the low-altitude UASbased platform to take many highly-overlapped images to produce a good mosaic, instead of just one or a few image shots by the high-altitude aerial platform. Such an UAS flight usually takes 10 to 30 minutes or even longer to complete; environmental lighting change during this time span cannot be ignored especially when spectral variations of various parts of a field are of interests. In this case study, we compared the visible reflectance of two aerial imagery – one generated from mosaicked UAS images, the other generated from a single image taken by a manned aircraft – over the same agricultural field to quantitatively evaluate their spectral variations caused by the different data acquisition strategies. Specifically, we (1) developed our customized ground calibration points (GCPs) and an associated radiometric calibration method for UAS data processing based on camera’s sensitivity characteristics; (2) developed a basic comparison method for radiometrically calibrated data from the two aerial platforms based on regions of interests. We see this study as a starting point for a series of following studies to understand the environmental influence on UAS data and investigate the solutions to minimize such influence to ensure data quality.

Paper Details

Date Published: 19 May 2017
PDF: 6 pages
Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 102180B (19 May 2017); doi: 10.1117/12.2263506
Show Author Affiliations
Yeyin Shi, Univ. of Nebraska-Lincoln (United States)
J. Alex Thomasson, Texas A&M Univ. (United States)
Chenghai Yang, USDA-ARS (United States)
Dale Cope, Texas A&M Univ. (United States)
Chao Sima, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 10218:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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