Research on farmland crop classification based on UAV multispectral remote sensing images

Abstract: UAV remote sensing technology is used to accurately and efficiently identify crops in farmland, providing technical support for obtaining crop category information in time and realizing precision agriculture.  Obtain multi-spectral images of farmland peach trees, corn, weeds and other ground objects with quadrotor equipped with a multi-spectral camera, and use the method of principal component analysis to transform the multi-spectral image, retain the first 2 spectral bands with the most information content, and calculate 16 textures from 7 vegetation indices, 6 reflectances and 2 principal component bands, totaling 29 The item feature is used as the feature parameter of the classification sample.  The ReliefF algorithm is used to screen the spectral features and texture features respectively.  The 11 features obtained (blue reflectivity, green reflectivity, red edge reflectivity, normalized green band difference vegetation index, enhanced vegetation index, ratio vegetation index, first One principal component correlation, second principal component synergy, information entropy, second moment, correlation).  from the screening form the A-group feature data set.  All the spectral features extracted from the image form the B-group feature data set and all the extracted texture features form the C-group feature data Set, all 29 features form the D feature data set.  SVM support vector machine is used to supervise and classify the 4 groups of feature data sets, and the accuracy of the classification results obtained is evaluated.  The accuracy of the supervised classification model trained on the single category feature data sets of B and C is poor, and the SVM supervised classification model trained on the feature data set of the ReliefF algorithm has a better classification effect, with an overall accuracy of 90.09%, and the Kappa coefficient of 0.86.  The SVM model trained on the feature data set D has the best classification effect, with an overall accuracy of 92.01%, and the Kappa coefficient of 0.89.  The ground object classification based on UAV multi-spectral images is efficient and feasible, which provides a reference for timely acquisition of crop planting structure in farmland. Keywords: UAV, Ground object identification, Multispectral remote sensing, ReliefF algorithm DOI:  10.33440/j.ijpaa.20210401.153 Citation: Yang D J, Zhao J, Lan Y B, Wen Y T, Pan F J, Cao D L, Hu C X, Guo J K.  Research on farmland crop classification based on UAV multispectral remote sensing images.  Int J Precis Agric Aviat, 2021; 4(1): 29–35.