Extraction method for single Zanthoxylum bungeanum in karst mountain area based on unmanned aerial vehicle visible-light images

Abstract

The efficient and rapid extraction of information about the growth period of the Zanthoxylum bungeanum maxim is one of the prerequisites for analyzing and mastering its growth trend. The 5-cm high-resolution visible-light red, green, blue (RGB) image of the Zanthoxylum bungeanum growing period is obtained based on the low-altitude aerial photography of a quad-rotor unmanned aerial vehicle (UAV) platform. The color space of the R-, G-, and B-channels is converted into the H-, S-, L-, and V-components, and the sensitivity of seven feature vectors to target features is analyzed. The combination of the R-band, H-component, and S-component (which distinguish ground objects very differently) is obtained as the identification feature. Motivated by this, the color space index (CSI) is built by three sensitive component features, which are segmented by the maximum class variance method (OTSU), filtered by majority filtering analysis, and processed by clump to obtain spots containing only the plants. The number of single plants completely separated was calculated by ArcGIS10.2 software, and the average area of a single plant was obtained. The number of conjoined plants was obtained by dividing the cluster by the average area of a single plant. The number of separated complete plants and cluster divisions is counted to get the final total amount of plants and compared with the excess green mainstream index (EXG), visible-light band difference vegetation index (VDVI), and normalized green red difference index (NGRDI). The results indicate the following: (1) the reflectance overlapping phenomenon between ground objects is usually encountered during the construction of the index using only the RGB channel features of the visible-light band. However, the H-, S-, L-, and V-components are merged when the original image features are changed by the color space, in which the images containing only the RGB band are enriched and more conducive to finding sensitive features to distinguish between features; (2) in the CSI extraction results of three experimental research areas and one test verification area, the overall accuracy exceeded 90% (kappa index over 0.90), the false extraction rate and leakage extraction rate were <10 % , and the results showed that the CSI had an effect on the identification and extraction of Zanthoxylum bungeanum; and (3) when the growth environment of Zanthoxylum bungeanum is disturbed by weeds, the CSI has more advantages than the EXG and VDVI.