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中国科学院大学学报 ›› 2014, Vol. 31 ›› Issue (5): 626-631.DOI: 10.7523/j.issn.2095-6134.2014.05.007

• 环境科学与地理学 • 上一篇    下一篇

景观破碎化区域土地覆被面向对象分类研究——以皖南山区为例

刘桂林1,2, 张落成1, 赵金丽1,2   

  1. 1. 中国科学院南京地理与湖泊研究所 湖泊与环境国家重点实验室, 南京 210008;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2013-02-26 修回日期:2013-09-25 发布日期:2014-09-15
  • 通讯作者: 张落成,E-mail:lchzhang@niglas.ac.cn
  • 基金资助:

    国家自然科学基金(41130750)、国家海洋公益专项(201005009-13)、中国科学院重点部署项目(KZZD-EW-10-04-2)和中国科学院南京地理与湖泊研究所135项目(NIGLAS2012135006)资助

Study on land-cover classification using object-oriented method in landscape fragmentation zone:in the mountainous region of southern Anhui

LIU Guilin1,2, ZHANG Luocheng1, ZHAO Jinli1,2   

  1. 1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2013-02-26 Revised:2013-09-25 Published:2014-09-15

摘要:

以景观破碎化的皖南山区为试验靶区,基于2009年4期的Landsat 5 TM与HJ-1/CCD遥感影像,结合野外调查数据,分析土地覆被类型在影像上的特征,挖掘出相应的光谱、纹理、空间及时相等信息,获取相关特征波段.基于See 5.0软件,根据训练样本筛选特征波段及组合并获取最佳阈值.基于面向对象分类软件,对TM影像进行尺度10、30的2级分割,参照最佳特征波段及阈值建立规则集并获取分类数据.结果表明:基于面向对象的多时相分类法能精确地获取土地覆被数据,精度均高于92%,其在以皖南山区为代表的景观破碎化区域适用性较高.

关键词: 多时相遥感影像, 面向对象分类, 破碎化, 皖南山区

Abstract:

Landsat 5 TM and HJ-1 CCD images in 2009 were selected for land-cover classification in mountainous region of southern Anhui. Combining with field survey data, we analyzed land-cover characteristics to dig out spectrum, texture, spatial and temporal information, and obtained the feature bands of the corresponding land-cover types. Using See 5.0 software, we oltained the optimal feature bands and thresholds of land-cover types based on training samples. The rule sets of land-cover classification were established using eCognition Developer 8.64 software. The results showed that, by using the object-oriented classification method based on multi-temporal remote sensing images, the land-cover classification with accuracies higher than 92% was acquired. This method is suitable for land-cover classification in the mountainous region of southern Anhui.

Key words: multi-temporal remote sensing imagery, object-oriented classification, fragmentation, mountainous region of southern Anhui

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