空间科学学报2024，Vol.44Issue(1)：103-113,11.DOI:10.11728/cjss2024.01.2023-0035
基于高光谱数据的密云水库水生态空间地物精细分类
Land Cover Classification from Hyperspectral Data in the Water Ecological Space of Miyun Reservoir
摘要
Abstract
With the acceleration of China's urbanization process,the problem of the structure and function of water ecological space has become increasingly severe.Monitoring the detailed distribution of land cover types in the key water ecological space is critical for their health assessment and future eco-logical planning.This study investigated a hybrid feature selection algorithm and GF-5 hyperspectral da-ta(with a spatial resolution of 30 m)to generate a fine land cover classification method for the water ecological space of Miyun Reservoir in Beijing.Firstly,the feature importance ranking was determined using the Random Forest(RF)algorithm and several feature subsets were generated with feature amount gradually carried out in a step size of 10.Then,the classification model was generated based on each subset using the RF algorithm.The feature subset that achieved the highest overall classification accuracy was determined as the initial feature subset.Next,the backward sequential selection algorithm was used to this initial subset to search for the best feature subset.Finally,the classification model of the water ecological space of Miyun Reservoir was generated based on the best feature subset and RF al-gorithm.To validate the advance of GF-5 hyperspectral data,this study also developed a classification model using Sentinel-2 multispectral data(with a spatial resolution of 10 m)for comparison.The results indicated that hyperspectral data achieved high classification accuracy(overall classification accuracy of 93.61%,and Kappa coefficient of 91.71%),especially in the accurate recognition of tree species(The pro-ducer's accuracy and user's accuracy of the chestnut forest are 81.25%and 73.03%,respectively).The re-flectance of shortwave infrared bands of GF-5 data has increased the differentiation between chestnut forests and other tree species.By contrast,Sentinel-2 data-based model achieved lower classification ac-curacy with an overall accuracy of 85.91%and a Kappa coefficient of 82.00%.This result indicated that although Sentinel-2 data has higher spatial resolution than GF-5 data,it still has difficulty identifying chestnut forests due to a lack of fine band information.The classification algorithm proposed in this study can provide accurate basic data for supporting the rational planning and management of water ecological spaces.关键词
遥感分类/高光谱数据/随机森林/水生态空间Key words
Remote sensing classification/Hyperspectral data/Random forest/Water ecological space分类
天文与地球科学引用本文复制引用
陈珠琳,贾坤,李添雨,张耀方,薛万来,谢营,吴迪,赵晨强,马利,王思棋..基于高光谱数据的密云水库水生态空间地物精细分类[J].空间科学学报,2024,44(1):103-113,11.基金项目
国家自然科学基金项目(42192581,42171318)和北京市科技计划课题项目(Z221100005222013)共同资助 （42192581,42171318）
