基于无人机RGB影像的玉米种植信息高精度提取方法

为探究易获取且成本低的超高分辨率无人机（Unmanned Aerial Vehicle，UAV）航拍 "红-绿-蓝"（Red-Green-Blue，RGB）彩色影像提取作物种植信息的方法，该研究选取植被指数、"色度-色饱和度-亮度"（Hue-Saturation-Intensity，HSI）色彩特征和纹理特征等3种特征，通过比较贝叶斯（Bayes）、K最邻近分类（K-Nearest Neighbor，KNN）、支持向量机（Support Vector Machine，SVM）、决策树（Decision Tree，DT）和随机森林（Random Forest，RF）共5种监督分类算法及不同特征组合的分类效果，以实现玉米种植信息的高精度提取。结果表明，使用单一种类特征或使用全部3种特征均不能获得最优的分类精度；将植被指数与HSI色彩特征或与纹理特征进行组合获得的总体分类精度（5种算法平均值）比仅使用植被指数获得的总体分类精度分别提高了4.2%和8.3%；在所有特征组合中，HSI色彩特征和纹理特征组合为最优选择，基于该特征空间的RF算法获得了最高的分类精度，总精度为86.2%，Kappa系数为0.793；基于RF算法进行降维并不能显著提高或降低分类精度（SVM除外），但所保留的特征因子可给出符合实际背景和意义的解释，并可提高分类结果的稳定性。研究结果可为基于无人机RGB影像的作物种植信息高精度提取提供方法参考。

High-precision extraction method for maize planting information based on UAV RGB images

Abstract: Ultra-high-resolution aerial images obtained from Unmanned Aerial Vehicles (UAVs) have widely been used to extract crop planting information in recent years. However, some high-resolution multispectral or hyperspectral images were usually costly and time-consuming for data processing. Therefore, it is very necessary to effectively use easily accessible and low-cost high-resolution RGB images, particularly to eliminate the common noises (e.g., shadows and bare land) for a better extraction accuracy of crop planting. In this study, a high-precision extraction method was proposed to obtain the maize planting information using 1.8 cm resolution UAV aerial orthophotos (i.e., RGB images). The experimental maize farm was located in Southeast Africa, where images were taken at noon during the maize growing season. The classification features were also selected from the aspects of the spectrum, color space, and image texture. Then, five types of classification were selected to extract maize planting information, including Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms). Firstly, an object-oriented interpreting platform (eCognition9.0 software) was selected to calculate the space transform of Hue, Saturation, and Intensity (HSI) color, eight types of RGB texture, and five vegetation indices, including the normalized green-red difference index, red-green ratio index, vegetation color verification index, visible-band difference vegetation index, and excess green vegetation index. Then, three types of feature space were constructed: 1) The first feature space was composed of three sub-feature spaces, i.e., vegetation indices, HSI color space features, and RGB image texture features; 2) The second feature space was composed of four sub-feature spaces, where three types of features were combined (i.e., vegetation indices, HSI color space, and RGB image texture) in pairs or total; 3) The third feature space was composed of the most optimal factors, where the dimension reduction was performed on the combination of all three types of features using RF. Subsequently, the RGB images were classified into three land-use types, including maize, bare land, and shadow. Bayes, KNN, SVM, DT, and RFs models were finally selected for the supervised classification with error matrix. The results showed that the optimal classification accuracy was obtained using neither a single feature nor all three types of features in total. More importantly, a combination of features was usually achieved higher accuracy than that of a single feature. Specifically, the best choice was the combination of HSI color and RGB image texture features using the RF, particularly with the total highest accuracy of 0.862 and a Kappa coefficient of 0.793. Additionally, the dimension reduction of features using RF models was neither significantly improved nor reduced classification accuracy (except for the SVM). However, the factors retained from the feature dimension reduction were easily explained suitable for the actual background and meaning. Furthermore, both classification efficiency and stability were improved greatly during this time. The finding can provide a specific solution for the high-precision extraction of crop planting information using UAV RGB images.