Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification

Authors: ZHANG, YANCHAO - Zhejiang Sci-Tech University; WEN, YANG - Zhejiang Sci-Tech University; SUN, YING - Cornell University; Chang, Christine; YU, JIYA - Zhejiang Sci-Tech University; ZHANG, WENBO - Zhejiang Sci-Tech University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/31/2021
Publication Date: 4/7/2021
Citation: Zhang, Y., Wen, Y., Sun, Y., Chang, C.Y., Yu, J., Zhang, W. 2021. Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification. Remote Sensing. 13:1411. https://doi.org/10.3390/rs13081411.
DOI: https://doi.org/10.3390/rs13081411

Interpretive Summary: Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of vegetation indices, derived from multispectral bands, for ground cover classification has been proven reliable and widely adopted. This study evaluates the performance of several machine learning methods for fusing information from multispectral imaging and derived vegetation indices in order to improve image classification of ground cover, using images collected by UAV over an almond plantation. Six methods were tested, including support vector machine (SVM), linear discriminant analysis, K-nearest neighbors, random forest, decision trees, and gradient boost. Of these, SVM performed the best. The findings also show that combining multispectral bands with vegetation indices improves classification accuracy beyond relying solely on either multispectral bands or vegetation indices alone.

Technical 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.