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Remote Sens. 2017, 9(6), 583; doi:10.3390/rs9060583

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

1
Department of Civil Engineering, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan
2
Agriculture Department, Chiayi County Government, NO.1, Sianghe 1st Rd, Taibao City 61249, Chiayi County, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Jan Dempewolf, Jyoteshwar Nagol, Min Feng and Clement Atzberger
Received: 20 March 2017 / Revised: 16 May 2017 / Accepted: 5 June 2017 / Published: 10 June 2017
View Full-Text   |   Download PDF [8144 KB, uploaded 10 June 2017]   |  

Abstract

Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment. View Full-Text
Keywords: rice lodging; unmanned aerial vehicle (UAV); image-based modeling; spectral and spatial hybrid image classification; decision tree classification; single feature probability rice lodging; unmanned aerial vehicle (UAV); image-based modeling; spectral and spatial hybrid image classification; decision tree classification; single feature probability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yang, M.-D.; Huang, K.-S.; Kuo, Y.-H.; Tsai, H.P.; Lin, L.-M. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery. Remote Sens. 2017, 9, 583.

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