Use of classification,and regression trees (CART) to classify remotely-sensed digital images

被引：0
|
作者：
Bittencourt, HR [1 ]
Clarke, RT [1 ]
机构：
[1] PUCRS, Dept Estatist, Fac Matemat, Porto Alegre, RS, Brazil
来源：
IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES | 2003年
关键词：
image processing; decision trees; CART; pattern recognition; high-dimensional data;
D O I：
暂无
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
Binary tree-structured rules can be viewed in terms of repeated splits of subsets of the feature space into two descendant subsets, starting from the entire feature space and ending in a partition of the feature space associated with each class. This paper presents a brief introduction to binary decision trees and shows results obtained in the classifying Landsat-TM and AVIRIS digital images.
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页码：3751 / 3753
页数：3
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