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基于WorldView-2影像数据对杭州西湖区绿地信息提取研究(PDF)

《西南林业大学学报》[ISSN:2095-1914/CN:53-1218/S]

期数:
2017年04期
页码:
156-166
栏目:
出版日期:
2017-06-30

文章信息/Info

Title:
Extraction of the Urban Green Space Based on WorldView-2 Images in West Lake District of Hangzhou
文章编号:
2095-1914(2017)04-0156-11
作者:
钱军朝12徐丽华12邱布布12陆张维12庞恩奇12郑建华3
1. 浙江农林大学环境与资源学院,浙江 临安 311300;
2. 浙江农林大学浙江省森林生态系统碳循环与固碳减排重点实验室,浙江 临安 311300;
3. 浙江农林大学风景园林与建筑学院,浙江 临安 311300
Author(s):
Qian Junchao12 Xu Lihua12 Qiu Bubu12 Lu Zhangwei12 Pang Enqi12 Zheng Jianhua3
1. College of Environment and Resource, Zhejiang A & F University, Lin′an Zhejiang 311300, China;
2. Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A & F University, Lin′an Zhejiang 311300, China;
3. College of Landscape and Architecture, Zhejiang A & F University, Lin′an Zhejiang 311300, China
关键词:
区域城市绿地信息J-M距离决策树 特征变量
Keywords:
region urban green space information J-M distance decision tree characteristic variable
分类号:
S757.4
DOI:
10.11929/j.issn.2095-1914.2017.04.023
文献标识码:
A
摘要:
以杭州市西湖区为例,根据研究区域地物在WorldView-2遥感影像的特征差异进行区域划分。在每个分区内采用不同的多尺度和方式进行分割,构建多层次结构,综合利用光谱、形状、纹理等特征变量;采用CART决策树分类算法,选择最优特征及节点阈值分区域对杭州市西湖区的植被绿地信息进行提取;采用Jeffries-Matusita (J-M)距离法,确定纹理窗口尺度并筛选纹理特征。结果表明:本研究利用可分离指数J-M距离法得到影像地物草地、农用地、灌木、乔木最佳纹理窗口尺寸分别为5×5、11×11、13×13、13×13,对纹理尺度的选择和纹理特征的降维极大地提高了信息提取的精度及效率;基于面向对象的CART决策树分类法的总体分类精度相比基于像元的最大似然法的精度从76.53%提高到88.56%,Kappa系数从0.711 7提高到0.862 3,绿地平均用户精度从72.73%提高到84.63%;同时比常规的面向对象的方法更快速灵活地确定分类特征及阈值,大幅度地提高了提取效率及精度。
Abstract:
According to the difference of objects in the WorldView-2 imagery in West Lake District of Hangzhou, sub-regions were divided. Within each partition, different multi-scale segmentation was used and a hierarchical structure was built. To make a comprehensive utilization of spectrum, shape and texture features of variables, the CART (classification and regression trees) decision tree classification algorithm was constructed to select the optimal characteristics and thresholds for each sub-region to map the entire green space of West Lake District. To determine the texture window size and optimize the texture features, the method of J-M (Jeffries-Matusita) distance was used. The results showed that with the method of J-M distance, the texture window size of grassland, agricultural land, shrubs and trees was 5×5, 11×11, 13×13, 13×13, respectively. It greatly improved the precision and efficiency of information extraction for the selection of texture window size and dimension of texture features. Comparing with the maximum likelihood method classification based on pixel, the overall accuracy was increased from 76.53% to 88.56%, and the kappa coefficient was improved from 0.711 7 to 0.862 3, the average user accuracy of green space was also increased from 72.73% to 84.63%; Comparing with the conventional object-oriented method, the proposed method is more quickly flexible to determine features and thresholds, greatly improving the efficiency and accuracy of classification.

参考文献/References

[1]仇江啸, 王效科, Qiu Jiangxiao, 等. 基于高分辨率遥感影像的面向对象城市土地覆被分类比较研究[J]. 遥感技术与应用, 2010, 25(5): 653-661.
[2]苏伟, 李京, 陈云浩, 等. 基于多尺度影像分割的面向对象城市土地覆被分类研究: 以马来西亚吉隆坡市城市中心区为例[J]. 遥感学报, 2007, 11(4): 521-530.
[3]Bhaskaran S, Paramananda S, Ramnarayan M. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data[J]. Applied Geography, 2010, 30(4): 650-665.
[4]陈君颖, 田庆久. 高分辨率遥感植被分类研究[J]. 遥感学报, 2007, 11(2): 221-227.
[5]孙晓艳, 杜华强, 韩凝, 等. 面向对象多尺度分割的SPOT5影像毛竹林专题信息提取[J]. 林业科学, 2013, 49(10): 80-87.
[6]张磊, 邵振峰. 改进的OIF和SVM结合的高光谱遥感影像分类[J]. 测绘科学, 2014, 39(11): 114-117.
[7]马娜, 胡云锋, 庄大方, 等. 基于最佳波段指数和JM距离可分性的高光谱数据最佳波段组合选取研究: 以环境小卫星高光谱数据在东莞市的应用为例[J]. 遥感技术与应用, 2010, 25(3): 358-365.
[8]杜泳, 张霄宇, 黄大松, 等. 以水体为观测目标的Worldview2融合方法评价[J]. 浙江大学学报 (工学版), 2015, 49(5): 993-1000.
[9]苏簪铀, 邱炳文, 陈崇成. 基于面向对象分类技术的景观信息提取研究[J]. 遥感信息, 2009(2): 42-46.
[10]关元秀, 程晓阳. 高分辨率卫星影像处理指南[M]. 北京: 科学出版社, 2008.
[11]李敏, 崔世勇, 李成名, 等. 面向对象的高分辨率遥感影像信息提取: 以耕地提取为例[J]. 遥感信息, 2008(6): 63-66.
[12]周春艳. 面向对象的高分辨率遥感影像信息提取技术[D]. 青岛: 山东科技大学, 2006.
[13]Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sensing of Environment, 1995, 51(3): 375-384.
[14]田庆久, 闵祥军. 植被指数研究进展[J]. 地球科学进展, 1998, 13(4): 327-333.
[15]S. K. McFEETERS. 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): 1425-1432.
[16]Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. Systems Man & Cybernetics IEEE Transactions on, 1973, 3(6): 610-621.
[17]Marceau D J, Howarth P J, Dubois J M M, et al. Evaluation of the grey-level co-occurrence matrix method for landcover classification using spot imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 1990, 28(4): 513-519.
[18]Steven E F, Derek R P. Spectral texture for improved class discrimination in complex terrain[J]. International Journal of Remote Sensing, 1989, 10(8): 1437-1443.
[19]Mausel P W. Optimum band selection for supervised classification of multispectral data[J]. Photogrammetric Engineering & Remote Sensing,1990,56(1): 55-60.
[20]张伐伐, 李卫忠, 卢柳叶, 等. SVM多窗口纹理土地利用信息提取技术[J]. 遥感学报, 2012, 16(1): 67-78.
[21]Breiman L, Friedman J, Stone C J, et al. Classification and Regression Trees[M]. CRC press, 1984.
[22]张晓娟, 杨英健, 盖利亚, 等. 基于CART决策树与最大似然比法的植被分类方法研究[J]. 遥感信息, 2010(2): 88-92.
[23]陈云, 戴锦芳, 李俊杰. 基于影像多种特征的CART决策树分类方法及其应用[J]. 地理与地理信息科学, 2008, 24(2): 33-36.

备注/Memo

备注/Memo:
收稿日期:2017-01-01; 修回日期:2017-03-30
基金项目:浙江省自然科学基金项目(LY15D010006)资助;国家自然科学基金项目(E080201)资助;浙江省林学一级重中之重学科学生创新计划项目 (201516) 资助。
第1作者:钱军朝(1990—),男,硕士生。研究方向:森林经理。Email: 460282787@qq.com
通信作者:徐丽华(1977—),女,博士,副教授。研究方向:城乡用地空间格局演变、城市化进程与城市绿地生态效应关系。Email: xulh99g@163.com
更新日期/Last Update: