王志盼,张清凌,钱静,肖雪.基于增强型水体指数的大棚遥感检测研究——以广东江门地区为例[J].集成技术,2017,6(2):11-21
基于增强型水体指数的大棚遥感检测研究——以广东江门地区为例
Greenhouse Extraction Based on the Enhanced Water Index——A Case Study in Jiangmen of Guangdong
  
DOI:
中文关键词:  大棚提取;增强型水体指数;土地利用;农业规划
英文关键词:greenhouse extraction; enhanced water index; land use; agricultural planning
基金项目:中国科学院百人计划项目(张清凌,2015);深圳市科技创新委员会基础研究项目(JCYJ20160429191303198)
作者单位
王志盼 中国科学院深圳先进技术研究院 深圳 518055;西南交通大学地球科学与环境工程学院 成都 611756 
张清凌 中国科学院深圳先进技术研究院 深圳 518055;中国科学院中亚生态与环境研究中心 乌鲁木齐 830011 
钱静 中国科学院深圳先进技术研究院 深圳 518055 
肖雪 西南交通大学地球科学与环境工程学院 成都 611756 
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中文摘要:
      基于遥感影像的农业大棚检测,能够快速获取大棚的空间分布情况与面积信息,对于农作物 监测、农业规划等具有重要的意义。现有大棚遥感检测算法大多依赖于高分辨率遥感影像或无人机航空影像,存在成本高、算法设计复杂等不足。针对此问题,文章基于 Landsat 影像提出了一种快速大棚检测算法。首先,根据冬季大棚内表面产生冷凝露水这一常见自然现象,提出了一种增强型水体指数;然后结合归一化植被指数与可见光光谱特征,来更好地描述大棚及其他地物的特征。在此基础上,设计一种简单高效的决策树分类器识别大棚。文章以广东江门大鳌镇为例,对不同年份的 Landsat影像展开实验并与其他方法进行对比。结果表明,文章所提方法有效地识别出了大棚,同时具有效率高、成本低、鲁棒性强的优点。
英文摘要:
      Greenhouse mapping has attracted much attention recently, especially in China where greenhouse practice has been growing dramatically. Remote sensing based greenhouse extraction methods can generate the geographical locations and spatial distribution of greenhouses efficiently. Most of the existing greenhouse extraction algorithms rely on high-resolution remote sensing images or aerial images, which are often expensive to obtain and require complicated algorithms to process. To solve this problem, this paper proposes a fast algorithm for greenhouse extraction based on Landsat images that are freely available. First, an enhanced water index was introduced to characterize winter greenhouse, based on an observed natural phenomenon that water vapor inside a greenhouse is usually condensed to form a layer of dew on the inner surface of the greenhouse plastic or glass. On one hand, the dew layer makes a greenhouse has a high water index value, which makes it to be distinguished easily from bare land. On the other hand, the dew layer increases a greenhouse’s reflectivity, which makes it different from natural water bodies. In order to extract greenhouses, a simple and efficient decision tree classifier was designed. Da’ao town of Jiangmen in Guangdong Province was chosen as an example, and the experiments were based on Landsat images taken in different years. The results show that the proposed method is effective in extracting greenhouses, with the advantages of high efficiency, low cost, and strong robustness.
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