skip to main content

DOE PAGESDOE PAGES

Title: Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INSmore » navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Lastly, experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.« less
Authors:
 [1] ; ORCiD logo [2] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Purdue Univ., West Lafayette, IN (United States); Kyungpook National Univ., Sangju (Korea)
Publication Date:
Grant/Contract Number:
AR0000593
Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 10; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Research Org:
Purdue Univ., West Lafayette, IN (United States). Lyles School of Civil Engineering
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 60 APPLIED LIFE SCIENCES; automated image registration; ortho-rectification; hyperspectral push-broom scanners; phenotyping; SURF; UAV-based agricultural management
OSTI Identifier:
1362128

Habib, Ayman, Han, Youkyung, Xiong, Weifeng, He, Fangning, Zhang, Zhou, and Crawford, Melba. Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery. United States: N. p., Web. doi:10.3390/rs8100796.
Habib, Ayman, Han, Youkyung, Xiong, Weifeng, He, Fangning, Zhang, Zhou, & Crawford, Melba. Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery. United States. doi:10.3390/rs8100796.
Habib, Ayman, Han, Youkyung, Xiong, Weifeng, He, Fangning, Zhang, Zhou, and Crawford, Melba. 2016. "Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery". United States. doi:10.3390/rs8100796. https://www.osti.gov/servlets/purl/1362128.
@article{osti_1362128,
title = {Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery},
author = {Habib, Ayman and Han, Youkyung and Xiong, Weifeng and He, Fangning and Zhang, Zhou and Crawford, Melba},
abstractNote = {Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Lastly, experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.},
doi = {10.3390/rs8100796},
journal = {Remote Sensing},
number = 10,
volume = 8,
place = {United States},
year = {2016},
month = {9}
}