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Improved Recognition of Spectrally Mixed Land Cover Classes Using Spatial Textures and Voting Classifications

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Computer Analysis of Images and Patterns (CAIP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2124))

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Abstract

Regenerating forest is important to account for carbon sink. Mapping regenerating forest from satellite data is difficult because it is spectrally mixed with natural forest. This paper investigated the combined use of texture features and voting classifications to enhance recognition of these two classes. Bagging and boosting were applied on Learning Vector Quantization (LVQ) and decision tree. Our results show that spatial textures improved separability. After applying voting classifications, class accuracy of decision tree increased by 5–7% and that of LVQ by approximately 3%. Substantial reduction (between 23% to 40%) of confusions between regenerating forest and natural forest were recorded. Comparatively, bagging is more consistent than boosting. An interesting observation is that even LVQ, a stable learner, was able to benefit from both voting classification algorithms.

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Author information

Authors and Affiliations

  1. Laboratory for Global Remote Sensing Studies Geography Department, University of Maryland, College Park, MD, 20742, USA

    Jonathan C.W. Chan & John R. G. Townshend

  2. Earth System Interdisciplinary Science Center and Geography Department, University of Maryland, College Park, MD, 20742, USA

    Ruth S. DeFries

Authors
  1. Jonathan C.W. Chan
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  2. Ruth S. DeFries
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  3. John R. G. Townshend
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Editor information

Editors and Affiliations

  1. Faculty of Electronics and Information Technology Institute of Radioelectronics, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Władysław Skarbek

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© 2001 Springer-Verlag Berlin Heidelberg

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Chan, J.C., DeFries, R.S., Townshend, J.R.G. (2001). Improved Recognition of Spectrally Mixed Land Cover Classes Using Spatial Textures and Voting Classifications. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_27

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  • DOI: https://doi.org/10.1007/3-540-44692-3_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42513-7

  • Online ISBN: 978-3-540-44692-7

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