{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:07:29Z","timestamp":1772042849567,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,9,24]],"date-time":"2016-09-24T00:00:00Z","timestamp":1474675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.<\/jats:p>","DOI":"10.3390\/rs8100796","type":"journal-article","created":{"date-parts":[[2016,9,26]],"date-time":"2016-09-26T10:02:44Z","timestamp":1474884164000},"page":"796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6498-5951","authenticated-orcid":false,"given":"Ayman","family":"Habib","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6586-8503","authenticated-orcid":false,"given":"Youkyung","family":"Han","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"},{"name":"School of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea"}]},{"given":"Weifeng","family":"Xiong","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Fangning","family":"He","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Zhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Melba","family":"Crawford","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1146\/annurev-arplant-050312-120137","article-title":"Future scenarios for plant phenotyping","volume":"64","author":"Fiorani","year":"2013","journal-title":"Annu. Rev. Plant Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.3390\/s130302830","article-title":"BreedVision\u2014A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding","volume":"13","author":"Busemeyer","year":"2013","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tao, V., and Li, J. (2007). Advances in Mobile Mapping Technology, Taylor & Francis.","DOI":"10.4324\/9780203961872"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Habib, A., Xiong, W., He, F., Yang, H., and Crawford, M. (2016). Improving Orthorectification of UAV-based Push-broom Scanner Imagery using Derived Orthophotos from Frame Cameras. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2016.2520929"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5006","DOI":"10.3390\/rs5105006","article-title":"Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected using a Lightweight UAV Spectral Camera for Precision Agriculture","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16688","DOI":"10.3390\/s150716688","article-title":"Towards the Development of a Smart Flying Sensor: Illustration in the Field of Precision Agriculture","volume":"15","author":"Hernandez","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field High-throughput Phenotyping: The New Crop Breeding Frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D Hyperspectral Information with Lightweight UAV Snapshot Cameras for Vegetation Monitoring: From Camera Calibration to Quality Assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"725","DOI":"10.3390\/rs70100725","article-title":"Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV based Goniometer","volume":"7","author":"Burkart","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3390\/agronomy4030349","article-title":"Proximal Remote Sensing Buggies and Potential Applications for Field based Phenotyping","volume":"4","author":"Deery","year":"2014","journal-title":"Agronomy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"513","DOI":"10.13031\/2013.12940","article-title":"Calibration of a Pushbroom Hyperspectral Imaging System for Agricultural Inspection","volume":"46","author":"Lawrence","year":"2003","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.08.026","article-title":"Diagnostic Mapping of Canopy Nitrogen Content in Rice based on Hyperspectral Measurements","volume":"126","author":"Inoue","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2012.05.002","article-title":"Remote Sensing of Sagebrush Canopy Nitrogen","volume":"124","author":"Mitchell","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"635","article-title":"Bundle Adjustment with Self-Calibration using Straight Lines","volume":"17","author":"Habib","year":"2002","journal-title":"Photogramm. Rec."},{"key":"ref_18","first-page":"25","article-title":"UAV Photogrammetry for Mapping and 3D Modeling\u2014Current Status and Future Perspectives","volume":"38","author":"Remondino","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11013","DOI":"10.3390\/rs61111013","article-title":"A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles","volume":"6","author":"Suomalainen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1002\/rob.21624","article-title":"Low-altitude Terrestrial Spectroscopy from a Pushbroom Sensor","volume":"33","author":"Lary","year":"2016","journal-title":"J. Field Robot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from Scale-invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"SURF Speeded Up Robust Features","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_23","unstructured":"Harris, C., and Stephens, M. (September, January 31). A Combined Corner and Edge Detector. Proceedings of the Alvey Vision Conference, Manchester, UK."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.1109\/TGRS.2011.2144607","article-title":"Uniform Robust Scale-invariant Feature Matching for Optical Remote Sensing Images","volume":"49","author":"Sedaghat","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5612","DOI":"10.1109\/TGRS.2013.2291001","article-title":"Parameter Optimization for the Extraction of Matching Points between High-resolution Multisensor Images in Urban Areas","volume":"52","author":"Han","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/LGRS.2011.2163491","article-title":"Multilevel SIFT Matching for Large-size VHR Image Registration","volume":"9","author":"Huo","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3850","DOI":"10.1080\/01431161.2011.636079","article-title":"A Robust Multisource Image Automatic Registration System based on the SIFT Descriptor","volume":"33","author":"Wang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.cageo.2007.10.005","article-title":"A Fast and Fully Automatic Registration Approach based on Point Features for Multi-source Remote-sensing Images","volume":"34","author":"Yu","year":"2008","journal-title":"Comput. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/LGRS.2012.2216500","article-title":"Registration of Optical and SAR Satellite Images by Exploring the Spatial Relationship of the Improved SIFT","volume":"10","author":"Fan","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.14358\/PERS.78.3.211","article-title":"Automatic Registration of High-resolution Images using Local Properties of Features","volume":"78","author":"Han","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.14358\/PERS.80.11.1041","article-title":"A Robust Image Matching Method based on Optimized BaySAC","volume":"80","author":"Kang","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1080\/01431161.2015.1030046","article-title":"Automatic and Accurate Registration of VHR Optical and SAR Images using a Quadtree Structure","volume":"36","author":"Han","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3390\/rs8010056","article-title":"Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery","volume":"8","author":"Long","year":"2016","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/LGRS.2015.2507982","article-title":"Feature-area Optimization: A Novel SAR Image Registration Method","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00127126","article-title":"Robust Regression Methods for Computer Vision: A Review","volume":"6","author":"Meer","year":"1991","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","unstructured":"He, F., and Habib, A. (2015, January 4\u20138). Target-based and Feature-based Calibration of Low-cost Digital Cameras with Large Field-of-view. Proceedings of the ASPRS Annual Conference, Tampa, FL, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"149","DOI":"10.5194\/isprsarchives-XL-1-149-2014","article-title":"Linear Approach for Initial Recovery of the Exterior Orientation Parameters of Randomly Captured Images by Low-cost Mobile Mapping System","volume":"1","author":"He","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/10\/796\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:31:43Z","timestamp":1760211103000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/10\/796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,24]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2016,10]]}},"alternative-id":["rs8100796"],"URL":"https:\/\/doi.org\/10.3390\/rs8100796","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,24]]}}}