Enhancing positional accuracy of airborne hyperspectral imagery with concurrent RGB images

C. Yang

Enhancing positional accuracy of airborne hyperspectral imagery with concurrent RGB images

In: Precision agriculture '25

Author:

C. Yang

C. Yang USDA Agricultural Research Service, Southern Plains Agricultural Research Center, 3103 F and B Road, College Station, Texas 77845, USA; chenghai.yang@usda.gov

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Type:: Chapter

Pages:: 418–425

DOI:: https://doi.org/10.1163/9789004725232_053

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Dar Hadith al Hassania

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Geometric distortions in airborne hyperspectral imagery directly affect crop parameter estimation and health assessment in precision agriculture. This study evaluated and enhanced the positional accuracy of orthorectified hyperspectral images using concurrently acquired RGB imagery as a reference. Imagery from a Headwall hyperspectral sensor was first orthorectified, and then registered with a mosaicked RGB image to determine the positional errors based on automatically generated tie points. Both polynomial and triangulation-based transformations were used to align the images. Results showed that the root mean square error for positional differences decreased from 4.1 m to 1.3 m for the polynomial model and to 0.5 m with triangulation. These findings provide a practical method for improving hyperspectral image accuracy and optimizing tie point selection in agricultural applications.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

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- Life Sciences
  - General
  - Agriculture & Environment

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Enhancing positional accuracy of airborne hyperspectral imagery with concurrent RGB images

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Precision agriculture '25

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