Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery

作者：Guoli Zhou, Zhongyun Ni, Yinbing Zhao, Junwei Luan · 发表于：Sensors · 年份：2022 · DOI：10.3390/s22145434 · 被引用次数：32 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Remote-Sensing Image Classification

Mapping the distribution of bamboo species is vital for the sustainable management of bamboo and for assessing its ecological and socioeconomic value. However, the spectral similarity between bamboo species makes this work extremely challenging through remote sensing technology. Existing related studies rarely integrate multiple feature variables and consider how to quantify the main factors affecting classification. Therefore, feature variables, such as spectra, topography, texture, and vegetation indices, were used to construct the XGBoost model to identify bamboo species using the Zhuhai-1 Orbita hyperspectral (OHS) imagery in the Southern Sichuan Bamboo Sea and its surrounding areas in Sichuan Province, China. The random forest and Spearman's rank correlation analysis were used to sort the main variables that affect classification accuracy and minimize the effects of multicollinearity among variables. The main findings were: (1) The XGBoost model achieved accurate and reliable classification results. The XGBoost model had a higher overall accuracy (80.6%), kappa coefficient (0.708), and mean F1-score (0.805) than the spectral angle mapper (SAM) method; (2) The optimal feature variables that were important and uncorrelated for classification accuracy included the blue band (B1, 464-468 nm), near-infrared band (B27, 861-871 nm), green band (B5, 534-539 nm), elevation, texture feature mean, green band (B4, 517-523 nm), and red edge band (B17, 711-720 nm); and (3) the XGBoost...