Modelling and representation issues in automated feature extraction from aerial and satellite images

被引:38
作者
Sowmya, A [1 ]
Trinder, J
机构
[1] Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia
[2] Univ New S Wales, Sch Geomat Engn, Sydney, NSW 2052, Australia
关键词
artificial intelligence; computer vision; knowledge; models; photogrammetry; remote sensing; representation; feature extraction; automation;
D O I
10.1016/S0924-2716(99)00040-4
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
New digital systems for the processing of photogrammetric and remote sensing images have led to new approaches to information extraction for mapping and Geographic Information System (GIS) applications, with the expectation that data can become more readily available at a lower cost and with greater currency. Demands for mapping and GIS data are increasing as well for environmental assessment and monitoring. Hence, researchers from the fields of photogrammetry and remote sensing, as well as computer vision and artificial intelligence, are bringing together their particular skills for automating these tasks of information extraction. The paper will review some of the approaches used in knowledge representation and modelling for machine vision, and give examples of their applications in research for image understanding of aerial and satellite imagery. (C) 2000 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:34 / 47
页数:14
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