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A Review of Modern Approaches to Classification of Remote Sensing Data

Part of the Remote Sensing and Digital Image Processing book series (RDIP,volume 18)

Abstract

This chapter presents an extensive review on the techniques proposed in the recent literature for the classification of remote sensing (RS) images. Automatic classification techniques for obtaining land-cover maps from RS images are usually based on supervised learning methods. Accordingly, we focus our attention on supervised techniques for the classification of different types of RS images acquired by new generation satellite sensors. Initially we analyze the critical problems related to different types of RS data and review the classification techniques that can overcome these problems. Then, the most recent methodological developments related to classification techniques in RS are addressed by focusing on semisupervised learning, active learning and domain adaptation approaches. Finally, the most promising research directions in RS data classification are discussed.

Keywords

  • Remote Sense
  • Hyperspectral Image
  • Radial Basis Function Neural Network
  • Target Domain
  • Domain Adaptation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Lorenzo Bruzzone .

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Bruzzone, L., Demir, B. (2014). A Review of Modern Approaches to Classification of Remote Sensing Data. In: Manakos, I., Braun, M. (eds) Land Use and Land Cover Mapping in Europe. Remote Sensing and Digital Image Processing, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7969-3_9

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