Decision tree regression for soft classification of remote sensing data

被引:420
作者
Xu, M
Watanachaturaporn, P
Varshney, PK [1 ]
Arora, MK
机构
[1] Syracuse Univ, Dept Elect Engn & Comp Sci, Syracuse, NY 13244 USA
[2] Indian Inst Technol, Dept Civil Engn, Roorkee 247667, Uttar Pradesh, India
基金
美国国家航空航天局;
关键词
non-parametric classification; decision tree regression; soft classification; classification accuracy;
D O I
10.1016/j.rse.2005.05.008
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data. Their implementation as a per-pixel based classifier to produce hard or crisp classification has been reported in the literature. Remote sensing images, particularly at coarse spatial resolutions, are contaminated with mixed pixels that contain more than one class on the ground. The per-pixel approach may result in erroneous classification of images dominated by mixed pixels. Therefore, soft classification approaches that decompose the pixel into its class constituents in the form of class proportions have been advocated. In this paper, we employ a decision tree regression approach to determine class proportions within a pixel so as to produce soft classification from remote sensing data. Classification accuracy achieved by decision tree regression is compared with those achieved by the most widely used maximum likelihood classifier, implemented in the soft mode, and a supervised version of the fuzzy c-means classifier. Root Mean Square Error (RMSE) and fuzzy error matrix based measures have been used for accuracy assessment of soft classification. (C) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码:322 / 336
页数:15
相关论文
共 25 条
[1]   Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels [J].
Bastin, L .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1997, 18 (17) :3629-3648
[2]   A fuzzy set-based accuracy assessment of soft classification [J].
Binaghi, E ;
Brivio, PA ;
Ghezzi, P ;
Rampini, A .
PATTERN RECOGNITION LETTERS, 1999, 20 (09) :935-948
[3]  
Breiman L., 1998, CLASSIFICATION REGRE
[4]  
Campbell J.B., 2002, INTRO REMOTE SENSING
[5]   A REVIEW OF ASSESSING THE ACCURACY OF CLASSIFICATIONS OF REMOTELY SENSED DATA [J].
CONGALTON, RG .
REMOTE SENSING OF ENVIRONMENT, 1991, 37 (01) :35-46
[6]  
FOODY GM, 1992, PHOTOGRAMM ENG REM S, V58, P1459
[7]   Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications [J].
Foody, GM ;
Arora, MK .
PATTERN RECOGNITION LETTERS, 1996, 17 (13) :1389-1398
[8]   Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data [J].
Foody, GM .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1996, 17 (07) :1317-1340
[9]  
FOODY GM, 1994, INT J REMOTE SENS, V15, P619, DOI 10.1080/01431169408954100
[10]   Estimation of sub-pixel land cover composition in the presence of untrained classes [J].
Foody, GM .
COMPUTERS & GEOSCIENCES, 2000, 26 (04) :469-478