资源一号02C星图像水体信息提取方法

引用本文

胡卫国, 孟令奎, 张东映, 樊志伟, 成建国, 李晓晖. 资源一号02C星图像水体信息提取方法[J]. 国土资源遥感, 2014,26(2): 43-47
HU Weiguo, MENG Lingkui, ZHANG Dongying, FAN Zhiwei, CHENG Jianguo, LI Xiaohui. Methods of water extraction from ZY-1 02C satellite imagery[J]. REMOTE SENSING FOR LAND & RESOURCES,2014,26(2): 43-47 复制到剪切板

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《国土资源遥感》编辑部

资源一号02C星图像水体信息提取方法

胡卫国¹, 孟令奎¹, 张东映¹, 樊志伟¹, 成建国², 李晓晖¹

1. 武汉大学遥感信息工程学院,武汉 430079

2. 水利部水利信息中心,北京 100053

第一作者简介: 胡卫国(1989-),男,硕士研究生,主要从事遥感监测方面的研究。Email:wghu@whu.edu.cn。

收稿日期: 2013-05-28

基金: 水利部公益性行业科研专项经费项目(编号: 201001046)资助

摘要

针对我国西部高原湖区资源卫星遥感监测与应用需求,开展资源一号02C星(ZY-1 02C)图像水体信息提取方法研究。用归一化植被指数和归一化差异水体指数进行龙羊峡库区水体信息提取,并将二者结合,提出决策树水体信息提取方法。以人工解译的水体区域为参考,对水体区域的整体提取结果、细节提取结果和误提取率进行统计和对比分析。实验结果表明,NDVI法易受到薄云的影响,但受冰雪和地形的影响较小; NDWI法在冰雪、薄云和山体阴影等成像条件下会受到不同程度的影响; 决策树法虽然受山体阴影等地形的影响较大,但是能有效地消除冰雪和薄云等气候环境的干扰。

关键词: ZY-1 02C星; 水体信息提取; NDVI; NDWI; 决策树

中图分类号:TP751.1 文献标志码:A 文章编号:1001-070X(2014)02-0043-05 doi: 10.6046/gtzyyg.2014.02.08

Methods of water extraction from ZY-1 02C satellite imagery

HU Weiguo¹, MENG Lingkui¹, ZHANG Dongying¹, FAN Zhiwei¹, CHENG Jianguo², LI Xiaohui¹

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

2. Water Information Center, Ministry of Water Resources, Beijing 100053, China

Abstract

Taking into consideration the needs of the remote sensing monitoring and application with Chinese resource satellite in west highland lakes, the authors carried out research on water extraction method by using ZY-1 02C satellite images. The two traditional spectral indexes i.e., the normalized difference vegetation index(NDVI)and the normalized differential water index(NDWI), were used to extract the water bodies in Longyangxia reservoir and, by combining these two kinds of water extraction method, a decision tree water extraction method was presented in this paper. Taking the manual interpretation of the water region as the reference,the authors used the overall area of the water extraction results, the detail extraction and the rate of error extraction results to make statistic and comparative analysis. The experimental results show that the NDVI method is susceptible to the influence of thin cloud, but is less affected by snow and terrain. Under the imaging conditions of snow,thin cloud and mountain shadows,the NDWI method is subject to different degrees of impact. In spite of the fact that it is susceptible to mountain terrain effects such as shadows,the method of the decision tree can effectively eliminate the interference of climate conditions such as snow and thin cloud.

Keyword: ZY-1 02C satellite; water information extraction; NDVI; NDWI; decision tree

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0 引言

随着空间信息科学的发展, 水体信息提取已成为遥感研究领域的主要分支之一^[1]。近年来, 我国对水体的遥感研究主要围绕Landsat TM/ETM⁺, SPOT, 中巴资源卫星和环境小卫星等几个主要的中、高分辨率卫星多光谱图像开展。徐涵秋^[2]在归一化差异水体指数(normalized difference water index, NDWI)的基础上提出改进的归一化差异水体指数(modified NDWI, MNDWI), 进行ETM⁺图像水体信息提取; 王培培^[3]利用ETM⁺数据, 通过光谱特征分析和归一化植被指数(normalized difference vegetation index, NDVI)实现水体信息自动提取; 都金康^[4]等提出SPOT图像决策树水体信息提取方法; 陈蕾等^[5]利用波谱差异关系提取出TM图像的水体分布信息; 沈金祥等^[6]利用面向对象的图像分析方法提取出了山区湖泊TM图像的水体信息; 韩晶等^[7]对SPOT多光谱图像分别采用单波段阈值法、谱间关系法、光谱指数法、光谱面积法和决策树法等5种方法进行水体信息提取; 范登科^[8]等对环境星CCD图像采用NDVI, NDWI和归一化差异综合水体指数(combined index of NDVI and NIR for water identification, CIWI)等3种方法进行水体信息提取和对比分析。现有的中、高分辨率卫星多光谱数据水体信息提取方法主要有单波段阈值法、谱间关系法、光谱指数法、面向对象法和决策树法等。

2011年12月22日, 我国资源一号02C卫星(简称ZY-1 02C星)在太原卫星发射中心成功发射升空。ZY-1 02C卫星设计寿命为3 a, 搭载有全色多光谱相机和全色高分辨率相机, 主要任务是获取全色和多光谱图像数据, 可广泛应用于国土资源调查、水利、农业、林业及生态环境等重要领域^[9]。如何充分利用ZY-1 02C星多光谱图像, 找到合适的水体信息提取方法还鲜有报道, 已成为资源卫星应用研究的一项重要课题。本文针对我国西部高原湖区资源卫星遥感监测与应用需求, 开展ZY-1 02C星图像水体提取方法的研究, 提出决策树水体提取方法。

1 研究区概况与数据源

选择我国青藏高原上的龙羊峡库区为研究区。该库区位于青海省共和县境内的黄河上游, 是黄河流经青海大草原后进入黄河峡谷区的第一峡口。它是中国库容量第二大的水库, 仅次于三峡水库^[10]。龙羊峡库区水体的研究, 对我国西部高原水体信息提取研究和对黄河电力和水资源调度以及防凌有一定的意义。

选取由水利部水利信息中心提供的覆盖龙羊峡库区(获取时间为2012年2月5日12:13)的2景ZY-1 02C星1级多光谱图像为数据源。该图像有绿、红和近红外3个波段, 空间分辨率均为10 m。通过中国资源卫星应用中心提供的RPC有理函数模型对上述2景图像进行了几何纠正, 此时图像的像元值是经过系统辐射校正和几何纠正的DN值。这2景图像均未能单独完全覆盖龙羊峡水库, 因此对其进行了镶嵌和区域裁剪, 并将经过预处理的图像数据导出成TIFF格式文件。

2 研究方法

根据ZY-1 02C星的数据特点, 选用NDVI^[8]和NDWI^[11]进行水体信息提取, 并结合这2种水体提取指数, 提出一种决策树水体信息提取方法。

2.1 光谱指数提取法

水体在近红外波段明显的比在红波段吸收性强, 二者的DN值差异较大, 故水体的NDVI值呈现负值。因此, 可用ZY-1 02C星图像的近红外波段和红波段组合来提取水体目标。归一化差异水体指数NDWI由Mcfeeters^[11]提出, 由于水体的反射率从可见光到近红外依次降低, 在近红外波段几乎无反射, 因此可用绿波段和近红外波段的反差组成的NDWI进行水体信息提取。同时, 由于植被在近红外波段具有很强的反射, 利用绿波段和近红外波段的组合还可以极大地抑制植被的影响, 从而将水体与其他地物区分开。

在进行水体指数运算时, 同一景图像的定标系数实际上被约分去掉, 因此可以用像元DN值代替表观反射率进行光谱指数计算。对计算得到的NDVI和NDWI图像进行灰度直方图分析, 选取合适阈值进行图像二值化, 以区分水体和背景物。NDVI和NDWI的灰度直方图如图1所示。

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	图1 光谱指数图像灰度直方图Fig.1 Histogram of NDVI and NDWI images

从图1可以看出, NDVI和NDWI的像元灰度值均为双峰分布, 占比例较大的波峰区域代表图像中的非水体像元, 而占比例较小的波峰区域代表水体像元。根据双峰灰度分布图的阈值选取经验, 选取2个波峰之间的平缓区间的谷底值作为阈值^[12], 得到NDVI和NDWI图像的二值化阈值分别为-0.156和0.257。

2.2 决策树提取法

本文在NDVI和NDWI方法的基础上, 提出了一种决策树水体提取方法。首先, 对计算得到的NDVI图像进行二值化。由于水体与含水量高的植被、浅滩及山体阴影等易造成错分现象, 为了最大程度地保护水体区域, 故应选取稍大的阈值。NDVI图像灰度直方图呈双峰分布, 为了使水体区域被最大程度地保留, 同时也不至于使非水体被错分为水体的概率过大, 故选择非水体起点值-0.127作为二值化的阈值。然后, 以二值化后的NDVI图像作为水体掩模, 将其和NDWI图像进行掩模运算, 得到NDWI掩模图像。经过这一步处理后, NDWI图像中的非水体地物得到很大程度的去除。最后, 利用经掩模运算后的NDWI图像灰度直方图选取合适的阈值, 对NDWI图像进行二值化处理。通过对NDWI掩模图像及其灰度直方图进行分析知, 灰度值在[0.19, 0.24]区间内的像元主要为受冰雪、薄云和山体阴影等影响的像元, 为了有效消除这些因素的影响, 阈值应在该区间内选取。以0.01为间隔, 依次选取0.19~0.24之间的值作为阈值进行图像二值化, 发现选择0.21作为阈值时水体提取结果最佳。图2为决策树水体提取的技术流程。

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	图2 决策树水体提取技术流程Fig.2 Technical flow chart of water extraction based on decision tree

3 结果与分析

3.1 提取结果目视解译

用NDVI, NDWI和决策树3种方法, 分别对龙羊峡库区ZY-1 02C星图像进行水体提取。由于龙羊峡不同年份和季节的水域变化较大, 难以真实地确定水体实际分布, 故用人工解译从原始图像中提取出龙羊峡库区水体矢量图, 将其作为实际水库区域的参考。3种方法的水体信息提取结果如图3所示。

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	图3 ZY-1 02C图像水体提取结果(黑线圈定的范围为人工解译的库区水体区域)Fig.3 Results of water extracted from ZY-1 02C image

从图3可以看出, 3种方法均很好地提取了西北部库岸线; 而在东北部和东南部沿岸, NDVI法对水库东北部的库岸和黄河河道存在较多的漏提取现象, 而对东南部湖岸有轻微的过提取现象。NDWI法对东北部湖岸和河道也有较多的漏提现象, 对东南部库岸有过提取现象; 但其漏提取程度比NDVI法低, 过提取程度比NDVI法高。决策树法很好地提取了东北部库岸区域, 只是在东北部黄河河道存在轻微的漏提取现象, 但在东南部库岸存在比NDVI和NDWI法都多的过提取现象。

3.2 提取结果细节对比

为了找到影响水体提取结果的原因, 分析了研究区域内除水体之外的地物类别。根据先验知识, 水体提取的精度主要受到冰雪、薄云、山体阴影和混合地物的影响。为了研究这些因素对水体信息提取结果的影响程度, 对3种方法提取结果的细节进行了对比分析, 如表1所示。

表1 库岸提取细节对比 Tab.1 Comparison in detail of lakeshore extraction

从表1可以看出:

1)NDVI法和决策树法受冰雪影响较小, 冰雪覆盖的库岸被较完整地提取出来; NDWI法受冰雪影响较大, 被冰雪覆盖的库岸存在较多漏提取现象。

2)受薄云影响, NDVI法存在较多的漏提取现象, 且出现较多噪声点; NDWI法的漏提取程度次之, 也出现部分噪声点; 决策树法将库岸较准确地提取出来, 几乎无误提取现象。

3)受山体阴影(白色椭圆内)的影响, NDWI法和决策树法存在较多的水体过提取现象, 即较多的山体阴影像元被误提取为水体像元; NDVI法只存在轻微的水体过提取现象, 但是较另外2种方法出现了更多的噪声点。

4)受山体阴影和薄云的混合影响, NDVI法和NDWI法出现很严重的水体漏提取现象; 决策树法较完整地提取出了河道轮廓, 但受到山体阴影的影响, 部分山体阴影像元被误提取为水体像元, 且水体区域出现部分噪声点。

综上所述, 冰雪对3种方法水体提取结果的影响程度为NDWI法> 决策树法> NDVI法; 薄云对水体提取结果的影响程度为NDVI法> NDWI法> 决策树法; 山体阴影对水体提取结果的影响程度为决策树法> NDWI法 > NDVI法。

3.3 误提率对比

在参照人工解译水体区域的情况下, 在水体提取的二值图像中, 实际为水体而被错分为背景地物的像元数占实际水体像元总数的百分比被称为“ 漏提率” , 实际为背景地物而被错分为水体的像元数占实际水体像元总数的百分比被称为“ 过提率” , 二者被统称为“ 误提率” , 其取值范围为[0, 1], 其值越接近于0, 代表漏提率或过提率越低, 提取效果越好; 其值越接近于1, 代表漏提率或过提率越高, 提取效果越差。本文中的3种水体提取方法的误提率见表2。

表2 3种水体信息提取方法的误提率 Tab.2 Error rates of water extraction by 3 methods

从表2可以看出, 决策树法的漏提率最小, NDWI法次之, NDVI法的漏提率最大; 而NDVI法的过提率最小, NDWI法次之, 决策树法的过提率最大。结合上文分析其原因可知, NDVI法和NDWI法易受薄云影响, 导致部分水体像元被误提取为背景像元; 而薄云对决策树法几乎无影响。薄云对这3种方法的影响程度依次为NDVI法> NDWI法> 决策树法, 故其水体的漏提率的大小顺序与此一致。同理, 水体信息提取的结果受到山体阴影不同程度的影响, 导致背景像元被误提取为水体像元。山体阴影的影响程度依次为决策树法> NDWI法> NDVI法, 因此呈现出过提取率的大小顺序为决策树法> NDWI法> NDVI法。

4 结论

1)本文以ZY-1 02C星图像为数据源, 用NDVI和NDWI这2种传统的光谱指数进行水体提取, 并结合2种光谱指数提出了决策树水体提取方法。

2)通过对3种方法的提取结果进行评价分析发现, NDVI法水体提取结果易受薄云的影响, 但能有效消除冰雪和山体阴影的影响; NDWI法的水体提取结果受冰雪、薄云和山体阴影的综合影响, 其中受冰雪和薄云的影响较大; 决策树法的水体提取结果受山体阴影的影响较为严重, 而受冰雪影响较小, 但能有效地消除薄云对水体提取结果的影响。

3)实验结果表明, 传统的光谱指数法能有效地提取地物类型明显的库岸, 受山体背景的影响较小, 但易受到云雪等成像气候条件的干扰; 决策树法虽然受山体等地形因素的影响较大, 但能有效地克服云雪等气候条件的影响。

本文采用3种方法均能较准确地提取水体信息, 但均需要人工干预及目视解译, 且阈值选取的精确性尚未得到验证。今后的研究重点将集中于能够消除地形因素影响的决策树水体提取方法及水体提取的自动化, 为ZY-1 02C星数据在水资源方面的研究和应用提供支持和参考, 以提高资源卫星在水资源方面的监测能力和社会效益。

The authors have declared that no competing interests exist.

参考文献

View Option

[1]	杨莹, 阮仁宗. 基于TM影像的平原湖泊水体信息提取的研究[J]. 遥感应用, 2010(3): 60-64. Yang Y, Ruan R Z. Extraction of plain lake water body based on TM imagery[J]. Remote Sensing Information, 2010(3): 60-64. [本文引用:1] [CJCR: 0.7]
[2]	徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5): 589-595. Xu H Q. A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J]. Journal of Remote Sensing, 2005, 9(5): 589-595. [本文引用:1] [CJCR: 0.992]
[3]	王培培. 基于ETM影像的水体信息自动提取与分类研究[J]. 首都师范大学学报: 自然科学版, 2009, 30(6): 75-79. Wang P P. Automatic extraction and classification of water body from ETM image[J]. Journal of Capital Normal University: Natural Science Edition, 2009, 30(6): 75-79. [本文引用:1] [CJCR: 0.3974]
[4]	都金康, 黄永胜, 冯学智, 等. SPOT卫星影像的水体提取方法及分类研究[J]. 遥感学报, 2001, 15(3): 214-219. Du J K, Huang Y S, Feng X Z, et al. Study on water bodies extraction and classification from SPOT image[J]. Journal of Remote Sensing, 2001, 15(3): 214-219. [本文引用:1] [CJCR: 0.992]
[5]	陈蕾, 邓孺孺, 陈启东, 等. 基于水质类型的TM图像水体信息提取[J]. 国土资源遥感, 2012, 24(1): 90-94. Chen L, Deng R R, Chen Q D, et al. The extraction of water body information from TM imagery based on water quality types[J]. Remote Sensing for Land and Resources, 2012, 24(1): 90-94. [本文引用:1]
[6]	沈金祥, 杨辽, 陈曦, 等. 面向对象的山区湖泊信息自动提取方法[J]. 国土资源遥感, 2012, 24(3): 84-91. Shen J X, Yang L, Chen X, et al. A method for object-oriented automatic extraction of lakes in the mountain area from remote sensing image[J]. Remote Sensing for Land and Resources, 2012, 24(3): 84-91. [本文引用:1]
[7]	韩晶, 邓喀中, 范洪冬. 利用波段合成研究SPOT多光谱影像水体提取方法[J]. 全球定位系统, 2012, 37(5): 76-80. Han J, Deng K Z, Fan H D. Using band synthesis to research SPOT multispectral image water extraction methods[J]. GNSS World of China, 2012, 37(5): 76-80. [本文引用:1] [CJCR: 0.326]
[8]	范登科, 李明, 贺少帅. 基于环境小卫星CCD影像的水体提取指数法比较[J]. 地理与地理信息科学, 2012, 28(2): 14-19. Fan D K, Li M, He S S. Comparison and research on different indices for water extraction based on CCD images from HJ satellite[J]. Geography and Geo-information Science, 2012, 28(2): 14-19. [本文引用:2] [CJCR: 1.313]
[9]	中国资源卫星应用中心. 资源一号02C卫星[EB/OL]. [2012-07-25]. http: //www. cresda. com/n16/n1130/n175275/175577. html. China Centre for Resources Satellite Data and Application. ZY - 1 02C satellite[EB/OL]. [2012-07-25]. http://www.cresda.com/n16/n1130/n175275/175577.html. [本文引用:1]
[10]	百度百科. 龙羊峡水库[EB/OL]. [2013-05-02]. http: //baike. baidu. com/view/475889. htm. Baidu Baike. Longyangxia reservoir[EB/OL]. [2013-05-02]. http://baike.baidu.com/view/475889.htm. [本文引用:1]
[11]	Mcfeeters S K. The use of the normalized difference water index(NDWI)in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7): 1420-1432. [本文引用:2]
[12]	汤国安, 杨晰, 等. ArcGIS地理信息系统空间分析实验教程[M]. 2版. 北京: 科学出版社, 2012: 112-131. Tang G A, Yang X, et al. ArcGIS geographic information systems spatial analysis experimental course[M]. 2nd. Beijing: Science Press, 2012: 112-131. [本文引用:1]
[13]	朱长明, 张新, 骆剑承, 等. 基于样本自动选择与SVM结合的海岸线遥感自动提取[J]. 国土资源遥感, 2013, 25(2): 69-74. Zhu C M, Zhang X, Luo J C, et al. Automatic extraction of coastline by remote sensing technology based on SVM and auto-selection of training samples[J]. Remote Sensing for Land and Resources, 2013, 25(2): 69-74. [本文引用:1]

2010

0.0

0.7

杨莹, 阮仁宗. 基于TM影像的平原湖泊水体信息提取的研究[J]. 遥感应用, 2010(3): 60-64.

Yang

, Ruan R

Extraction of plain lake water body based on TM imagery[J]. Remote Sensing Information, 2010(3): 60-64.

以洪泽湖Landsat TM影像为例,分析了利用单波段阈值法和多波段增强图阈值法进行水体信息提取的差异,从而确定出不同时期不同用途所采用的最佳水体综合提取方法,即综合利用多波段谱间关系(TM2+TM3>TM4+TM5)和单波段TM5建立起适合于平原湖泊水域的水体提取方法.

... 0 引言随着空间信息科学的发展,水体信息提取已成为遥感研究领域的主要分支之一^[1] ...

2005

0.0

0.992

徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5): 589-595.

Xu H

A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J]. Journal of Remote Sensing, 2005, 9(5): 589-595.

A modified normalized difference water index(MNDWI) has been proposed in this paper based on the normalized difference water index(NDWI) of Mcfeeters (1966), which uses MIR(TM5) instead of NIR(TM4) to construct the MNDWI. The MNDWI has been tested in the ocean, lake and river areas with the background of built-up lands and/or vegetated lands, and with both clean and polluted water bodies using Landsat TM/ETM imagery. This reveals that the MNDWI can significantly enhance the water information, especially in the area mainly with built-up land as background. The MNDWI can depress the built-up land information effectively while highlighting water information, and accurately extract the water body information from the study areas. While the enhanced water information using the NDWI always has been mixed with built-up land noise and the area of a water body extracted based on the index is thus overestimated. Therefore, the NDWI is not suitable for enhancing and extracting water information in built-up land-dominated areas. Furthermore, the MNDWI can reveal subtle features of water more efficiently than the NDWI or other visible spectral bands do due largely to its wider dynamic data range. The application of the MNDWI in the Xiamen image has achieved an excellent result. The MNDWI image successfully reveals significant non-point pollution of the water surrounding the Xiamen Island due to agricultural activities. In addition, taking the advantage of the ratio computation, the MNDWI can remove shadow noise from water information without using sophisticated procedures, which is otherwise difficult to be removed.

在对M cfeeters提出的归一化差异水体指数(NDWI)分析的基础上,对构成该指数的波长组合进行了修改,提出了改进的归一化差异水体指数MNDWI(M odified NDWI),并分别将该指数在含不同水体类型的遥感影像进行了实验,大部分获得了比NDWI好的效果,特别是提取城镇范围内的水体。NDWI指数影像因往往混有城镇建筑用地信息而使得提取的水体范围和面积有所扩大。实验还发现MNDWI比NDWI更能够揭示水体微细特征,如悬浮沉积物的分布、水质的变化。另外,MNDWI可以很容易地区分阴影和水体,解决了水体提取中难于消除阴影的难题。

... 徐涵秋^[2]在归一化差异水体指数(normalized difference water index,NDWI)的基础上提出改进的归一化差异水体指数(modified NDWI,MNDWI),进行ETM⁺图像水体信息提取 ...

2009

0.0

0.3974

王培培. 基于ETM影像的水体信息自动提取与分类研究[J]. 首都师范大学学报: 自然科学版, 2009, 30(6): 75-79.

Wang P

Automatic extraction and classification of water body from ETM image[J]. Journal of Capital Normal University: Natural Science Edition, 2009, 30(6): 75-79.

在综合分析各种水体信息提取方法的基础上,以烟台市为例,利用ETM遥感影像数据,通过光谱特征分析和NDVI识别水体信息,并在各种专业软件的支持下,将水体与其它地物类型区分开来,实现水体信息的自动提取,最后利用形状指数、面积以及其它特征对水体进行分类,并对分类结果做出精度评价.

... 王培培^[3]利用ETM⁺数据,通过光谱特征分析和归一化植被指数(normalized difference vegetation index,NDVI)实现水体信息自动提取 ...

2001

0.0

0.992

都金康, 黄永胜, 冯学智, 等. SPOT卫星影像的水体提取方法及分类研究[J]. 遥感学报, 2001, 15(3): 214-219.

Du J

, Huang Y

, Feng X

, et al. Study on water bodies extraction and classification from SPOT image[J]. Journal of Remote Sensing, 2001, 15(3): 214-219.

The remote sensing mechanism and spectral characteristics of water bodies and other objects in SPOT 4 imagery were analyzed. Due to the similar infrared spectral response feature of water bodies to those of the objects under the mountain shadows in the imagery, thresholding for infrared bands is not suitable for the extraction of water bodies. The decision tree algorithm including several classifiers was put forward, which can extract water bodies effectively and easily. The terrain information and geometric features such as perimeter, area, and shape, which are frequently used in visual interpretation, can be used to classify water bodies into subclass. The decision tree algorithm including several classifiers associated with terrain and geometric information was developed to classify water bodies. Jiangning county of Jiangsu Province was selected as a case study area, SPOT 4 (XI, 1999.1) and contour data of the area were used as information sources, the results after applying the proposed approach were satisfactong.

文中分析了水体及其它主要的地物的光谱特征在SPOT曩像中的表征特征，发现大多数地形阴影与水体的光谱特征具有一定的相似性，因而用单一的方法很难有效地提取山区的水体，经过研究，发现用决策树分类方法，在各节点设计不同的分类器，可以有效地提取山区中的水体，由于不同类型的水体其面积，周长，形状等几何特征各有特点，并且与地貌类型有一定的关系，因而可利用水体的空间特征信息，对提取的水体进行分类，其方法也选用决策树分类方法，分类结果令人满意。

... 都金康^[4]等提出SPOT图像决策树水体信息提取方法 ...

2012

0.0

陈蕾, 邓孺孺, 陈启东, 等. 基于水质类型的TM图像水体信息提取[J]. 国土资源遥感, 2012, 24(1): 90-94.

Chen

, Deng R

, Chen Q

, et al. The extraction of water body information from TM imagery based on water quality types[J]. Remote Sensing for Land and Resources, 2012, 24(1): 90-94.

The lightness values of three types of water, i.e. ordinary, eutrophic and seriously polluted, and vegetation in the shadow of the hill were analyzed in this paper. The results show that the lightness value of TM4 is lower than that of TM3 for ordinary water, whereas things are opposite for vegetation in the shadow of the hill; the eutrophic water contaminated by phytoplankton has strong reflection in TM4, and seriously polluted water has strong absorption in visible band, which the lightness value of TM4 is higher than that of TM3. Thus the eutrophic and polluted water couldn’t be distinguished from vegetation in the shadow of the hill by comparison between TM3 and TM4. According to the extraction method of classification of water quality type the spectral characteristics of the water,the authors set up the thresholds to distinguish various types of water quality, vegetation in the shadow of the hill and other ground objects,and extracted the water distribution information from TM imagery quickly,accurately and efficiently.

通过对一般水体、富营养化水体和严重污染水体与山体阴影在TM各波段图像上的亮度值进行分析比较可知: 一般水体在TM4图像上的亮度值小于TM3波段的,而山体阴影则相反; 由于富营养化水体中的浮游植物在TM4波段具有强反射特征,严重污染水体对可见光具有强吸收作用,造成了这两类水体在TM4图像上的亮度值大于TM3波段的,因而无法通过比较TM3和TM4波段图像像元亮度值来区分水体和山体阴影。实验证明,采用对水质类型进行分类的提取方法,根据水体的影像特征设立相应的阈值,能将各种水质类型的水体与山体阴影等其他地物区分开来。可以快速、准确、有效地提取TM图像上的水体分布信息。

... 陈蕾等^[5]利用波谱差异关系提取出TM图像的水体分布信息 ...

2012

0.0

沈金祥, 杨辽, 陈曦, 等. 面向对象的山区湖泊信息自动提取方法[J]. 国土资源遥感, 2012, 24(3): 84-91.

Shen J

, Yang

, Chen

, et al. A method for object-oriented automatic extraction of lakes in the mountain area from remote sensing image[J]. Remote Sensing for Land and Resources, 2012, 24(3): 84-91.

Traditional water-body information extraction is mainly based on the reflection and absorption spectral characteristics of the water body. By building and using all kinds of spectral index models which respond to the water spectral characteristics more obviously, the water body could be extracted in pixel-level. However, different types of water bodies have significantly different spectral and spatial patterns as well as spatial distributions. As for the mountain area image, the shadows of such objects as the mountain, snow, ice and bare rock make these global water spectral index models fail to get a satisfactory result. The object-oriented image analysis carries out remote sensing image segmentation first, and then analyzes the global and local characteristics of the water in such aspects as spectral and spatial patterns, spatial distribution, and spatial relationships so as to build the water extraction decision ruleset; finally, the water body information is extracted with the ruleset automatically. The Landsat TM image water extraction experiment in the eCognition software shows that the method can completely avoid the emergence of some errors of "sporadic water body" which often exist in the pixel-level threshold value extraction, and the mountain area lakes could be extracted automatically and efficiently, with the accuracy up to 95% or even higher in the cloud-free case.

传统的水体信息提取主要利用水体反射与吸收光谱特征构建各种光谱指数模型,进行全局像元级的提取。然而,不同水体类型的光谱、空间形态与空间分布特征均有显著差异。对于山区图像而言,山体阴影、冰雪、裸岩等地物的干扰使全局性水体光谱指数模型难以取得很好的提取精度。面向对象的图像分析方法通过对遥感图像进行分割,从全域—局部上耦合分析水体的光谱、空间形态、空间分布与空间关系等特征,构建了通用性强的湖泊信息提取规则集,最终实现湖泊水体信息的自动化提取。通过eCognition软件对Landsat TM图像的实验结果表明,该方法可以完全避免像元级阈值水体信息提取中出现的一些错误的"零星水体",自动且高效地提取出了山区湖泊水体信息,在无云情况下提取精度达95%以上。

... 沈金祥等^[6]利用面向对象的图像分析方法提取出了山区湖泊TM图像的水体信息 ...

2012

0.0

0.326

韩晶, 邓喀中, 范洪冬. 利用波段合成研究SPOT多光谱影像水体提取方法[J]. 全球定位系统, 2012, 37(5): 76-80.

Han

, Deng K

, Fan H

Using band synthesis to research SPOT multispectral image water extraction methods[J]. GNSS World of China, 2012, 37(5): 76-80.

SPOT多光谱影像仅有4个波段,光谱信息相对较少,通常的水体提取方法受到波段数的限制,通过波段合成增加SPOT多光谱影像可用的波段数,不仅增加了水体提取方法的种类,而且,通过对矿区塌陷地积水现象进行水体提取试验,结果表明:波段合成对SPOT影像的水体提取情况有一定程度的改善,水体提取更完整,误提现象较少。

... 韩晶等^[7]对SPOT多光谱图像分别采用单波段阈值法、谱间关系法、光谱指数法、光谱面积法和决策树法等5种方法进行水体信息提取 ...

2012

0.0

1.313

范登科, 李明, 贺少帅. 基于环境小卫星CCD影像的水体提取指数法比较[J]. 地理与地理信息科学, 2012, 28(2): 14-19.

Fan D

, Li

, He S

Comparison and research on different indices for water extraction based on CCD images from HJ satellite[J]. Geography and Geo-information Science, 2012, 28(2): 14-19.

针对湖北省大东湖水网水环境遥感监测需求,开展基于环境小卫星CCD影像的内陆湖库水体提取指数方法研究.以常见的3种水体识别指数为模型,参照使用TM、MODIS等典型传感器进行水体提取的波段选择方式,通过对环境小卫星CCD影像4个波段地物光谱信息的变化趋势分析,发现各波段对水体目标的区分能力；使用指数模型和相应阈值分别计算获得水体特征图像,并以目视判读结合精度评价指标的方式对提取结果进行对比分析.实验结果表明,归一化差异综合水体指数的目视判别效果最好,且具有水陆区分度高和误识别率低的特点,适用于解决水体目标快速提取问题.

... 范登科^[8]等对环境星CCD图像采用NDVI,NDWI和归一化差异综合水体指数(combined index of NDVI and NIR for water identification,CIWI)等3种方法进行水体信息提取和对比分析 ...

... 2 研究方法根据ZY-1 02C星的数据特点,选用NDVI^[8]和NDWI^[11]进行水体信息提取,并结合这2种水体提取指数,提出一种决策树水体信息提取方法 ...

2012

0.0

... ZY-1 02C卫星设计寿命为3 a,搭载有全色多光谱相机和全色高分辨率相机,主要任务是获取全色和多光谱图像数据,可广泛应用于国土资源调查、水利、农业、林业及生态环境等重要领域^[9] ...

2013

0.0

... 它是中国库容量第二大的水库,仅次于三峡水库^[10] ...

1996

0.0

... 2 研究方法根据ZY-1 02C星的数据特点,选用NDVI^[8]和NDWI^[11]进行水体信息提取,并结合这2种水体提取指数,提出一种决策树水体信息提取方法 ...

... 归一化差异水体指数NDWI由Mcfeeters^[11]提出,由于水体的反射率从可见光到近红外依次降低,在近红外波段几乎无反射,因此可用绿波段和近红外波段的反差组成的NDWI进行水体信息提取 ...

2012

0.0

... 根据双峰灰度分布图的阈值选取经验,选取2个波峰之间的平缓区间的谷底值作为阈值^[12],得到NDVI和NDWI图像的二值化阈值分别为-0 ...

2013

0.0

朱长明, 张新, 骆剑承, 等. 基于样本自动选择与SVM结合的海岸线遥感自动提取[J]. 国土资源遥感, 2013, 25(2): 69-74.

Zhu C

, Zhang

, Luo J

, et al. Automatic extraction of coastline by remote sensing technology based on SVM and auto-selection of training samples[J]. Remote Sensing for Land and Resources, 2013, 25(2): 69-74.

The timely and accurate automatic extraction of coastline from satellite remote sensing imagery is one of the important applications of remote sensing technology and has great significance for management planning of the sea area. Because the spectral characteristics of coastal water are susceptible to regional environment,the traditional method of normalized difference water index(NDWI)threshold segmentation may easily misclassify water as land in the process of separation of land and water, which will seriously affect the accuracy of shoreline extraction. In this paper,on the basis of NDWI model,the authors proposed an automatic coastline extraction method based on classification sample auto-selection and support vector machine(SVM). Firstly,through the NDWI calculation and global threshold segmentation,the initial water distribution information is obtained. And then,the classification samples are selected automatically under the control of NDWI information. Thirdly,the water are separated from the land by using SVM classifier. The last step is to fill small terrestrial water body units and track coastline automatically. The experimental results show that this method can effectively enhance the capability of coastal water identification and improve the accuracy and automation of the coastline extraction from remote sensing imager.

利用卫星遥感手段自动、快速、准确地测定海岸线动态信息是遥感应用的一个重要领域,对海域管理规划具有重要意义。由于近岸水体光谱特征受区域环境影响较大,在水陆分离过程中,利用传统的归一化差值水体指数(normalized difference water index,NDWI)阈值分割法时,一部分近岸水体易被错分为陆地,严重影响了岸线提取精度。为此,在NDWI模型的基础上,提出了基于样本自动选择与支持向量机(support vector machine,SVM)的海岸线遥感自动提取算法。首先进行NDWI计算与全局阈值分割,实现水体信息的初步提取; 再通过NDWI信息控制初始样本的自动选择; 然后利用SVM分类器对水体再次分类,实现海陆分离; 最后填充小的陆地水体单元,实现岸线自动跟踪。实验结果表明,该方法能有效增强对近岸水体的识别能力,提高海岸线遥感提取的精度和自动化程度。