Abstract
Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine learning methods. We acquired multispectral images over an almond plantation using a UAV. First, a multispectral orthoimage was generated from the acquired multispectral images using SfM (Structure from Motion) photogrammetry methods. Eleven types of vegetation indexes were proposed based on the multispectral orthoimage. Then, 593 data points that contained multispectral bands and vegetation indexes were randomly collected and prepared for this study. After comparing six machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, Linear Discrimination Analysis, Decision Tree, Random Forest, and Gradient Boosting), we selected three (SVM, KNN, and LDA) to study the fusion of multi-spectral bands information and derived vegetation index for classification. With the vegetation indexes increased, the model classification accuracy of all three selected machine learning methods gradually increased, then dropped. Our results revealed that that: (1) spectral information from multispectral images can be used for machine learning-based ground classification, and among all methods, SVM had the best performance; (2) combination of multispectral bands and vegetation indexes can improve the classification accuracy comparing to only spectral bands among all three selected methods; (3) among all VIs, NDEGE, NDVIG, and NDVGE had consistent performance in improving classification accuracies, and others may reduce the accuracy. Machine learning methods (SVM, KNN, and LDA) can be used for classifying almond plantation using multispectral orthoimages, and fusion of multispectral bands with vegetation indexes can improve machine learning-based classification accuracy if the vegetation indexes are properly selected.
Highlights
While many studies used machine learning methods to build models based on spectral bands for ground classification [23], the nonlinear information contained in vegetation indexes has barely been fused into ground classification models
This study showed that a properly selected combination of Vis with original multispectral bands could improve the accuracy of the trained model, and a reasonable VI combination could further improve it
This paper describes the application of a Unmanned Aerial Vehicles (UAVs)-based five-band multispectral camera for the classification of three different ground covers, trees, soil, and shadow at first
Summary
Vegetation indices have long been used in remote sensing [19,20,21] because they are simple, intuitionistic, and effective ways to model the ground cover reflectance. In drone-based remote sensing, sensor and ground resolution are improved, vegetation indices alone cannot provide sufficient accuracy for classification [22]. While many studies used machine learning methods to build models based on spectral bands for ground classification [23], the nonlinear information contained in vegetation indexes has barely been fused into ground classification models. A rapid and accurate shadow classification is needed for almost all remote sensing platforms, including satellite, aircraft, and UAVs. Machine learning is widely adopted in remote sensing for ground classification, texture segmentation. We comprehensively compared the results trained with vegetation indexes and determined the proper selection protocol for classification
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