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Proceedings Paper

Remote sensing image classification development in the past decade
Author(s): Yan Li; Li Yan; Jin Liu
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Paper Abstract

Remote sensing image classification is a prerequisite for remote sensing applications, such as thematic mapping, urban planning, forest management, environment monitoring, disaster warning and assessment, military target recognition. Over the last decade there has been noticeable shift in remote sensing image classification with the extension of remote sensing imagery sources as well as the development of pattern recognition methods. This paper discusses the changes in remote sensing classification from two aspects: basic thought and new classification algorithms. The basic thought of remote sensing classification has changed from per-pixel multispectral-based approaches to multiscale object-based approaches. New classification algorithms include support vector machine, evolutionary algorithm, fuzzy clustering algorithm, as well as Artificial Neural Networks. At last this paper highlights the future research and application directions of remote sensing image classification.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941D (30 October 2009); doi: 10.1117/12.832872
Show Author Affiliations
Yan Li, Wuhan Univ. (China)
Li Yan, Wuhan Univ. (China)
Jin Liu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing
Faxiong Zhang; Faxiong Zhang, Editor(s)

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