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国土资源遥感  2020, Vol. 32 Issue (3): 71-79    DOI: 10.6046/gtzyyg.2020.03.10
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
一种抑制裸地的不透水面指数构建
曹勇1(), 陶于祥1(), 邓陆1, 罗小波1,2
1.重庆邮电大学计算机科学与技术学院,重庆 400065
2.重庆市气象科学研究所,重庆 401147
An impervious surface index construction for restraining bare land
CAO Yong1(), TAO Yuxiang1(), DENG Lu1, LUO Xiaobo1,2
1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065,China
2. Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
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摘要 

目前,不透水面光谱指数提取不透水面区域的方法因其简洁、快速等优势而得到广泛应用。但是,利用光谱指数提取不透水面区域的方法存在裸地和城市不透水面区域容易混淆的缺点。针对这个问题,根据不透水面和裸地与水体、植被在Landsat8 OLI影像的第4、第5和第6波段的光谱特征差异创建不透水面与裸地区域指数(impervious surface and bareness area index, ISBAI)。基于ISBAI和裸地区域指数(bareness area index, BAI) 构建一种新型的不透水面指数,称为抑制裸地的不透水面指数(bareness-restrained impervious surface index, BRISI); 然后使用改进的双窗口变步长搜寻法(improved double-window flexible pace search, IDFPS)确定最优阈值,从而进行不透水面区域的提取。选取重庆市(山地城市)和西安市(平原城市)作为研究区,对BRISI的提取精度进行评估,并与其他常用的几种不透水面指数进行了对比。实验结果表明,BRISI在重庆实验区和西安实验区的提取精度分别达到86.8%和88.4%,同参与对比的所有的指数相比,BRISI提取精度最高; 同时,BRISI还消除了裸地在不透水面区域提取中的影响,克服了其他不透水面指数难以区分裸地和不透水面区域的问题。

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曹勇
陶于祥
邓陆
罗小波
关键词 不透水面裸地抑制裸地的不透水面指数(BRISI)改进的双窗口变步长搜寻法(IDFPS)    
Abstract

At present, the method of extracting the impervious surface area based on the impervious surface area according to the impervious surface spectral index has been widely used because of its concision and speed. However, the method of extracting impervious surface by spectral index has the disadvantage that bare land and impervious surface are easily confused. To tackle this problem, the authors created impervious surface and bareness area index (ISBAI) according to the spectral feature difference of impervious surface, bare land and water body as well as vegetation in the 4, 5 and 6 bands of Landsat8 OLI images. Based on ISBAI and bareness area index (BAI), the authors built a new type of impervious surface index, called the bareness - restrained impervious surface index (BRISI). Improved double-window flexible pace search (IDFPS) method was used to determine the optimal threshold, and impervious surface extraction was performed. Chongqing (a mountain city) and Xi’an (a plain city) were selected as the research area to evaluate the accuracy of BRISI extraction in comparison with other commonly used impervious surface indices. The experimental results show that the extraction accuracy of BRISI in the experimental area of Chongqing and Xi’an experimental area reach 86.8% and 86.8% respectively, in comparison with the accuracy of all other indices that took part in the contrast, BRISI extraction accuracy is the highest. Meanwhile, BRISI also eliminates the influence of bare land in the construction area extraction, and overcomes the problem that it is difficult for other impervious surface indices to distinguish bare land from impervious surface.

Key wordsimpervious surface    bare land    BRISI(bareness-restrained impervious surface index)    IDFPS(improved double-flexible pace search)
收稿日期: 2019-09-09      出版日期: 2020-10-09
:  TP79  
基金资助:国家自然科学基金项目“城市地表温度降尺度模型及热岛时空演变规律研究”(41871226);重庆市博士后特别资助项目“城市热岛降尺度模拟及多尺度特征研究”(Xm2016081);重庆市高技术产业研发项目“空间信息智能处理关键技术研究”(D2018-82)
通讯作者: 陶于祥
作者简介: 曹 勇(1992-),男,硕士研究生,研究方向为城市建筑区域提取。Email: 2289909272@qq.com
引用本文:   
曹勇, 陶于祥, 邓陆, 罗小波. 一种抑制裸地的不透水面指数构建[J]. 国土资源遥感, 2020, 32(3): 71-79.
CAO Yong, TAO Yuxiang, DENG Lu, LUO Xiaobo. An impervious surface index construction for restraining bare land. Remote Sensing for Land & Resources, 2020, 32(3): 71-79.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.03.10      或      https://www.gtzyyg.com/CN/Y2020/V32/I3/71
Fig.1  研究区影像
(Landsat8 B5(R),B4(G),B3(B)波段合成影像)
序号 波段 波长范围/μm 空间分辨率/m
B1 海岸 0.433~0.453 30
B2 蓝光 0.450~0.515 30
B3 绿光 0.525~0.600 30
B4 红光 0.630~0.680 30
B5 近红外 0.845~0.885 30
B6 短波红外1 1.560~1.660 30
B7 短波红外2 2.100~2.300 30
B8 全色 0.500~0.680 15
B9 卷云 1.360~1.390 30
B10 热红外1 10.60~11.19 100
B11 热红外2 11.50~12.51 100
Tab.1  Landsat8 OLI波段信息
Fig.2  地物反射率曲线
Fig.3  ISBAI和BAI指数图像亮度
研究区 不透水面 裸地 水体 植被
研究区1 1.685 1.288 -0.297 0.511
研究区2 1.861 0.923 -0.192 0.407
Tab.2  2个研究区典型地物的BRISI均值
Fig.4  基于Landsat8影像的研究区1不透水面光谱指数专题信息比较
Fig.5  基于Landsat8影像的研究区2不透水面光谱指数专题信息比较
Fig.6  研究区1 基于不同光谱指数的不透水面专题信息
Fig.7  研究区2基于不同光谱指数的不透水面专题信息
研究区 指数 阈值 生产者
精度/%
使用者
精度/%
总体精
度/%
Kappa
研究区1 BRISI 0.149 89.7 87.2 86.8 0.724
NDBI -0.069 91.2 62.9 63.6 0.434
IBI 0.152 90.8 76.2 73.1 0.515
CBI 0.254 83.3 80.1 83.5 0.681
EBBI 0.268 83.6 80.8 82.9 0.627
NDISI 0.162 82.3 81.1 82.1 0.693
研究区2 BRISI 0.248 88.3 89.3 88.4 0.729
NDBI 0.026 91.4 59.1 58.2 0.399
IBI 0.469 90.2 63.5 65.1 0.492
CBI 0.245 87.7 83.4 84.7 0.687
EBBI 0.351 82.5 69.3 71.4 0.592
NDISI 0.188 83.1 84.0 82.0 0.655
Tab.3  其他指数和BRISI的精度评估
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