spatial and spectral hybrid image classification for rice lodging assessment through uav imagery

Clicks: 235
ID: 214844
2017
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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.
Reference Key
yang2017remotespatial Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ming-Der Yang;Kai-Siang Huang;Yi-Hsuan Kuo;Hui Ping Tsai;Liang-Mao Lin
Journal Journal of pharmacological sciences
Year 2017
DOI
10.3390/rs9060583
URL
http://www.mdpi.com/2072-4292/9/6/583
Keywords
decision tree classification unmanned aerial vehicle (uav)

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