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Multispectral Satellite Image Understanding pp 19–39Cite as

Linearized Vegetation Indices

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Most remote sensing satellite sensors offer multispectral images besides panchromatic images. This multispectral information is extremely valuable to detect various objects in the image. In this book, we summarize the multispectral information via vegetation and shadow–water indices. In the following chapters, we benefit from these indices in land use classification and house and street network detection. In this chapter, we consider the popular vegetation index based on the above observations, the normalized difference vegetation index (NDVI). Although there have been many indices proposed in the literature, theNDVI remains one of the most popular in remote sensing applications. We first introduce a statistical framework for theNDVI. We then propose a solution to the nonlinearity (saturation) problem based on this statistical explanation. For completeness, we then apply the same framework to the 3D and 4D feature spaces of blue, green, red, and near-infrared bands.

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Notes

  1. 1.

    The figures in this chapter are obtained from our previous work [1]. Here, they appear with the kind permission of IEEE.

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Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering, Yeditepe University, 26 Ağustos Yerleşimi, 34755, Kayisdagi, Istanbul, Turkey

    Assoc. Prof. Cem Ünsalan

  2. Dept. Electrical, Comp. & Systems Eng., Rensselaer Polytechnic Institute, 8th Street 110, 12180, Troy, NY, USA

    Prof. Kim L. Boyer

Authors
  1. Assoc. Prof. Cem Ünsalan
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  2. Prof. Kim L. Boyer
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Correspondence to Cem Ünsalan or Cem Ünsalan .

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Ünsalan, C., Boyer, K.L. (2011). Linearized Vegetation Indices. In: Multispectral Satellite Image Understanding. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-667-2_3

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  • DOI: https://doi.org/10.1007/978-0-85729-667-2_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-666-5

  • Online ISBN: 978-0-85729-667-2

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