A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

被引：106

作者：

Huang, C

Townshend, JRG

机构：

[1] Univ Maryland, Dept Geog, College Pk, MD 20742 USA

[2] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA

来源：

INTERNATIONAL JOURNAL OF REMOTE SENSING | 2003年 / 24卷 / 01期

基金：

美国国家科学基金会; 美国国家航空航天局;

关键词：

D O I：

10.1080/01431160305001

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al. (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

引用

收藏

页码：75 / 90

页数：16

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