Potential Evaluation of High Spatial Resolution Multi-Spectral Images Based on Unmanned Aerial Vehicle in Accurate Recog
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RESEARCH ARTICLE
Potential Evaluation of High Spatial Resolution Multi-Spectral Images Based on Unmanned Aerial Vehicle in Accurate Recognition of Crop Types Lei Li1,2 • Xingming Zheng1 • Kai Zhao1 • Xiaofeng Li1 • Zhiguo Meng3 • Chunhua Su4 Received: 6 February 2018 / Accepted: 10 August 2020 Ó Indian Society of Remote Sensing 2020
Abstract The accurate acquisition of farmland planting information is the basis of precision agriculture. Collecting remote sensing data via unmanned aerial vehicle (UAV) is a convenient method to obtain precision agricultural information because of the high spatiotemporal resolution and flexibility. A quadrotor UAV equipped with a SEQUOIA sensor (one multi-spectral sensor and one RGB lens) was operated over the Jingyuetan agricultural area with five land cover types on September 4, 2017, to investigate the equipment’s feasibility for crop identification. To evaluate the effects of different data and classification methods on the accuracy of crop type classification, three combinations were tested: MDC ? Four (Mahalanobis distance classifiers based on four-band reflectance), MDC ? VIs (Mahalanobis distance classifiers based on Vegetation Indices) and MLC ? VIs (maximum likelihood classifiers based on Vegetation Indices). The accuracy of the different classification methods was 83.06% (MDC ? Four), 89.17% (MDC ? VIs) and 92.60% (MLC ? VIs). The MLC ? VIs scheme was the most accurate, as it could partially overcome the influence of shadow and flattened. Since the reflectivity of different bands varied, all kinds of objects on the ground could be distinguished. This result revealed that multi-spectral UAV technology has the potential to identify crop type at the sub-meter spatial resolution, with the MLC based on the VIs. Keywords UAV Vegetation index Crop classification Multi-spectral reflectance
Introduction Crop classification and identification is the basis for accurate acquisition of farmland information and the division of crop planting areas. It plays an important role in the implementation of precise pesticide application, disease prevention, yield estimation, and crop planting structure adjustment (Steinberger et al. 2009; Zhang and Kovacs & Xingming Zheng [email protected] 1
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
4
ChangChun NewBlue Tech Co, Changchun 130000, China
2012; Erena et al. 2016). With the development of precision agriculture, it has become possible to estimate farmland boundaries and the types of crop that are planted from a distance (Steinberger et al. 2009; Zhang and Kovacs 2012; Erena et al. 2016). Technology using remote sensing has become an essential tool in agricultural research (Boryan and Craig 2005; Bailey and Boryan 2010; Zhang et al. 2017). At present, information about specific planting cycles and boundaries in f
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