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

  • Authors:

  • D. Lu;Q. Weng

  • Affiliations:

  • Center for the Study of Institutions, Population, and Environmental Change, Indiana University, Bloomington, IN 47408, USA;Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, USA

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2007

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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.