The accuracy of research on land use/cover change (LUCC) is determined directly by the accuracy of land use classification derived from aerial and satellite images.In analysis of the factors of accuracy of current remote sensing image classification, some methods were introduced to study new trends of classification modes.Some previous studies showed that the speed and accuracy of QUEST (Quick, Unbiased, and Efficient Statistical Tree) decision tree classification were superior to those of other decision tree classifications. On the basis of this approach, the research classified the Landsat TM-5 images in Lijiang, Yunnan province.This paper compared the result with that of maximum likelihood image classification.The overall accuracy was 90.086%, which was higher than the overall accuracy (85.965%) of CART (Classification And Regression Tree).Meanwhile, the Kappa efficient was 0.849, which was higher than the Kappa efficient (0.760) of CART. Therefore, it is concluded that in the complex terrain area such as in mountainous regions, the choice of QUEST decision tree classification on TM image would improve the accuracy of land use classification. This type of classification decision tree can precisely obtain new classification rules from integrated satellite images, land use thematic maps, DEM maps and other field investigation materials.Simultaneously, the method can also help users to find new classification rules in multidimensional information, and to build decision tree classifier models. Furthermore, the methods, including a large number of high-resolution and hyperspectral image data, integrated multi-sensor platform, multi-temporal remote sensing image, the pattern recognition and data mining of spectral and texture features, and auxiliary geographic data, will become a trend
WU Jian-sheng, PAN Kuang-yi, PENG Jian, HUANG Xiu-lan
. Research on the accuracy of TM images land-use classification based on QUEST decision tree: A case study of Lijiang in Yunnan[J]. GEOGRAPHICAL RESEARCH, 2012
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DOI: 10.11821/yj2012110005
[1] 李小文,刘素红。遥感原理与应用。北京:科学出版社,2008.104～105.
[2] 韩鹏,龚健雅,李志林,等。遥感影像分类中的空间尺度选择方法研究。遥感学报,2010,14(3):507～518.
[3] 黎夏,叶嘉安。基于神经网络的元胞自动机及模拟复杂土地利用系统。地理研究,2005,24(1):19～27.
[4] 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。遥感学报,2007,11(4):521～530.
[5] 曹丽琴,李平湘,张良培,等。基于多地表特征参数的遥感影像分类研究。遥感技术与应用,2010,25(1):38～44
[6] 赵萍,冯学智,林广发。SPOT卫星影像居民地信息自动提取的决策树方法研究。遥感学报,2003,7(4):309～315.
[7] Vaudour E,Carey V A,Gilliot J M.Digital zoning of South African viticultural terroirs using bootstrapped decision treeson morphometric data and multitemporal SPOT images.Remote Sensing of Environment,2010,114:2940～2950.
[8] 赵清,郑国强,黄巧华。基于神经网络模型技术的南京市主城区城市森林遥感调查。地理研究,2006,25(3):468～476.
[9] 彭建,李丹丹,张玉清。基于GIS和RUSLE的滇西北山区土壤侵蚀空间特征分析——以云南省丽江县为例。山地学报,2007,25(5):548～556.
[10] 申文明,王文杰,罗海江,等。基于决策树分类技术的遥感影像分类方法研究。遥感技术与应用,2007,22(3):333～338.
[11] 潘琛,杜培军,罗艳。一种基于植被指数的遥感影像决策树分类方法。计算机应用,2009,29(3):777～780.
[12] 朱江洪,李江风,叶菁。利用决策树工具的土地利用类型遥感识别方法研究。武汉大学学报:信息科学版,2011,36(3):301～305.
[13] Lim T S,Loh W Y,Shih Y S.A comparison of prediction accuracy,complexity,and training time of thirty-threeold and new classification algorithms.Machine Learning,2000,40:1～7.
[14] Loh W Y,Shih Y S.Split selection methods for classification trees.Statistica Sinica,1997,7:815～840.
[15] 那晓东,张树清,李晓峰,等。基于QUEST决策树兼容多源数据的淡水沼泽湿地信息提取。生态学杂志,2009,28(2):357～365.
[16] Yang X,Chapman G A,Young M A,et al.Using Compound Topographic Index to Delineate Soil Landscape Facets from Digital Elevation Models for Comprehensive Coastal Assessment.Paper presented at the MODSIM.2005International Congress on Modelling and Simulation.
[17] 李双成,高伟明,周巧富,等。基于小波变换的NDVI与地形因子多尺度空间相关分析。生态学报,2006,26(12):4198～4203.
[18] 李杭燕。时间序列NDVI数据集重建方法研究。兰州:兰州大学硕士学位论文,2010.
[19] Kross A,Fernandes R,Seaquist J.The effect of the temporal resolution of NDVI data on season onset dates andtrends across Canadian broadleaf forests.Remote Sensing of Environment,2011,115:1564～1575.
[20] 赵萍,傅云飞,郑刘根,等。基于分类回归树分析的遥感影像土地利用/覆被分类研究。遥感学报,2005,9(6):708～716.
[21] 陈君颖,田庆久。高分辨率遥感植被分类研究。遥感学报,2007,11(2):221～227.
[22] 邸向红,王周龙,张明明,等。基于决策树的蓬莱市土地覆盖信息提取。山东国土资源,2009,25(11):52～56.
[23] 陈百明,张凤荣。我国土地利用研究的发展态势与重点领域。地理研究,2011,30(1):1～9.
