摘要
本文选取珠海一号影像数据,以哨兵二号数据为参考,将平朔矿区按权属划分五个子研究区,采用面向对象结合最邻近特征的分类方法,融合光谱特征、纹理特征、植被指数等特征因子,对珠海一号矿区土地利用分类精度进行评价。研究结果表明:(1)珠海一号分类总体精度为78.87%,Kappa系数为0.7285;哨兵二号分类总体精度为78.38%,Kappa系数为0.7203,珠海一号分类精度略高于哨兵二号;(2)部分耕地、草地、裸地产生误分,可能由于选取影像成像时间过晚地物特征相似导致。总体来说,珠海一号高光谱数据光谱及空间分辨率高,具有应用于矿区生态监测、复垦区植被演替特征等研究的潜力。
In this paper,the Zhuhai-1 image data is selected,and the Sentinel-2 data is used as a reference to divide the Pingshuo mining area into five sub-study areas according to ownership.The object-oriented classification method combined with the nearest features is adopted,and the spectral features,texture features,vegetation index,etc.are integrated.The characteristic factor is used to evaluate the accuracy of land use classification in Zhuhai-1 mining area.The research results show that:(1)The overall classification accuracy of Zhuhai-1 is 78.87%,and the Kappa coefficient is 0.7285;the overall classification accuracy of Sentinel-2 is 78.38%,and the Kappa coefficient is 0.7203.The classification accuracy of Zhuhai-1 is slightly higher than that of Sentinel-2.(2)The misclassification of some cultivated land,grassland and bare land may be caused by the similarity of the features of the ground objects when the imaging time is too late.In general,Zhuhai-1 hyperspectral data has high spectral and spatial resolution,and has the potential to be applied to research on ecological monitoring of mining areas and vegetation succession characteristics in reclamation areas.
作者
王宏宇
周伟
官炎俊
胡文琛
WANG Hong-yu;ZHOU Wei;GUAN Yan-jun;HU Wen-chen(School of Land Science and Technology,China University of Geosciences Beijing,Beijing 100083,China;Key Laboratory of Land Consolidation and Rehabilitation,Ministry of Natural Resources,Beijing 100035,China;Technology Innovation Center for Ecological Restoration in Mining Areas,Ministry of Natural Resources,Beijing 100083,China)
出处
《山东农业大学学报(自然科学版)》
北大核心
2022年第1期98-108,共11页
Journal of Shandong Agricultural University:Natural Science Edition
基金
国家自然科学基金:黄土高原大型露天煤矿区复垦土壤空间异质性特征及指示意义(41571508)。
关键词
珠海一号
高光谱遥感
矿区分类
Zhuhai-1
hyperspectral remote sensing
mining area classification