| 基于HJ卫星数据与面向对象分类的土地利用/覆盖信息提取 |
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| 引用本文： | 朱永森,曾永年,张 猛.基于HJ卫星数据与面向对象分类的土地利用/覆盖信息提取[J].农业工程学报,2017,33(14):258-265. |
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| 作者姓名： | 朱永森 曾永年 张 猛 |
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| 作者单位： | 1. 中南大学地球科学与信息物理学院，长沙 410083；2. 中南大学空间信息技术与可持续发展研究中心，长沙 410083,1. 中南大学地球科学与信息物理学院，长沙 410083；2. 中南大学空间信息技术与可持续发展研究中心，长沙 410083,1. 中南大学地球科学与信息物理学院，长沙 410083；2. 中南大学空间信息技术与可持续发展研究中心，长沙 410083 |
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| 基金项目： | 国家自然科学基金项目（41171326, 40771198）；中南大学中央高校基本科研业务费专项资金（2017zzts775） |
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| 摘 要： | 土地利用/覆盖信息是区域气候与环境研究的基础,是土地资源规划与管理、合理开发与保护的信息保障。为此,该文选取长株潭城市群核心区为试验区,以时间序列HJ卫星影像为数据源,首先构建了时间序列归一化植被指数(normalized difference vegetation index,NDVI)、时间序列光谱第一主成分(first principal component,PC1)数据集,通过J-M(Jeffries-Matusita)距离变量可分离性分析结合地表覆盖的物候特征,确定最佳时序HJ组合数据;其次,采用面向对象的随机森林算法对研究区土地利用/覆盖信息进行分类,并对分类结果进行精度评价与比较分析。研究结果表明:采用时间序列HJ组合数据与面向对象的分类方法,提取城市土地利用/覆盖信息的总体精度和Kappa系数分别达到91.55%和0.90,其中水田、水浇地、旱地、林地、建设用地的生产者精度均达到90%及以上;相对于时间序列基于像元分类、单时相面向对象的分类方法,该文提出的土地利用/覆盖信息提取方法的总体分类精度和Kappa系数分别提高了2.26%、0.02和6.82%、0.08,有效提高了区域土地利用/覆盖信息提取的精度,为大范围土地利用/覆盖精细化分类提供了有效的途径。
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| 关 键 词： | 土地利用 遥感 分类 时间序列 面向对象 土地利用/覆盖分类 城市区域 HJ卫星 |
| 收稿时间： | 2017/1/19 0:00:00 |
| 修稿时间： | 2017/7/5 0:00:00 |
Extract of land use/cover information based on HJ satellites data and object-oriented classification |
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| Zhu Yongsen,Zeng Yongnian and Zhang Meng.Extract of land use/cover information based on HJ satellites data and object-oriented classification[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(14):258-265. |
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| Authors: | Zhu Yongsen Zeng Yongnian and Zhang Meng |
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| Institution: | 1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China;
2. Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China,1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China;
2. Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China and 1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China;
2. Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China |
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| Abstract: | Abstract: Land use/cover information is the basis for the study of regional climate and environment, and the information security of land resources planning and management. However, the accuracy of urban areas land use/cover information extraction is significantly affected by the high heterogeneity of land surface. Remote sensing technology, providing large-scale, timely, continuous and comprehensive measurements, has become an important means of land use/cover information extraction. HJ satellites, with high temporal and spatial resolution and wide coverage, provide a new way to fast and accurately extract large-scale land use/cover information. They have been widely used in land use/cover classification, crop information extraction, wetlands information extraction, and so on. The object-oriented classification method, which makes full use of the spectral information of remote sensing images and takes into account the spatial distribution characteristics and correlations of geographical objects, can compensate for the deficiency of traditional pixel-based classification methods. This study developed a supervised classification method for regional land use based on the object-oriented random trees algorithm to quickly extract land surface information with low cost and high precision. We selected the Changsha-Zhuzhou-Xiangtan core area as the study area and used the multi-temporal and multi-spectral information of HJ satellite CCD (charge-coupled device) data. Firstly, high quality HJ-CCD data (10 phases in total) were selected, and preprocessed by radiometric calibration, atmospheric correction, accurate geometric correction and image registration. The time series of normalized difference vegetation index (NDVI) and of the first principal component (PC1) were calculated, and their results overlapped each other. The best time series of HJ classification data were determined by the J-M (Jeffries-Matusita) distance variable separability analysis, combined with land cover of study area, and phenotypic characteristics difference of different vegetation. HJ data of the February, May, July, September, October and December phases are the best data combinations for land use/over information extraction in this study. Then the e-Cognition''s multi-scale segmentation algorithm was employed to segment the HJ-NDVI, HJ-PC1, HJ-PC2 (the second principal component) of the best time series combination. The urban land use/cover information was classified by the object-oriented random forest algorithm. Finally, the accuracy of the algorithm was evaluated, and compared with that of the time series pixel-based classification and single-phase object-oriented classification. The results indicate that the land use/cover information extracted by the object-oriented classification method using time series HJ data is consistent with the real situation on range and distribution of each land type, and with less speckle noise. The overall accuracy and Kappa coefficient of this method are 91.55% and 0.90 respectively. Specifically, the accuracy is higher than 90% for the paddy field, irrigated land, dry land and forest, and is close to 90% for building land. Compared with the time-series pixel-based classification and single-phase object-oriented classification methods, the overall classification accuracy and Kappa coefficient of the proposed method are increased by 2.26%, 0.02 and 6.82%, 0.08 respectively. This means the best time series HJ combination data can fully utilize the seasonal spectral differences of different vegetation types, which avoid the spectral similarity among different vegetations in single-phase image. So the proposed method can effectively improve the accuracy of land use/cover information extraction in urban areas. |
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| Keywords: | remote sensing land use classification time series object-oriented land use/cover classification urban areas HJ satellites |
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