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A survey of image classification methods and techniques for improving classification performance

Lu, D., Weng, Q.·2007·International Journal of Remote Sensing
DOI 10.1080/01431160600746456
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Abstract

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy.

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Metadata

Authors
Lu, D., & Weng, Q.
Venue
International Journal of Remote Sensing
Publisher
Informa UK Limited
Published
2007-03
Volume
28
Issue
5
Pages
823-870
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
DOI
10.1080/01431160600746456
OpenAlex
W2168809519
Cited by
3,402
References
0
FWCI
153.27
Type
journal article
Language
en
Open access
closed

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Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. https://doi.org/10.1080/01431160600746456

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