A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation

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The recent and continuing construction of multi and hyper spectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The reduction of this voluminous data to useful intermediate forms is necessary both for downlinking all those bits and for interpreting them. Smart onboard hardware is required, as well as sophisticated earth bound processing. A segmented image (in which the multispectral data in each pixel is classified into one of a small number of categories) is one ... continued below

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14 p.

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Theiler, J. & Gisler, G. July 1, 1997.

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The recent and continuing construction of multi and hyper spectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The reduction of this voluminous data to useful intermediate forms is necessary both for downlinking all those bits and for interpreting them. Smart onboard hardware is required, as well as sophisticated earth bound processing. A segmented image (in which the multispectral data in each pixel is classified into one of a small number of categories) is one kind of intermediate form which provides some measure of data compression. Traditional image segmentation algorithms treat pixels independently and cluster the pixels according only to their spectral information. This neglects the implicit spatial information that is available in the image. We will suggest a simple approach; a variant of the standard k-means algorithm which uses both spatial and spectral properties of the image. The segmented image has the property that pixels which are spatially contiguous are more likely to be in the same class than are random pairs of pixels. This property naturally comes at some cost in terms of the compactness of the clusters in the spectral domain, but we have found that the spatial contiguity and spectral compactness properties are nearly orthogonal, which means that we can make considerable improvements in the one with minimal loss in the other.

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14 p.

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OSTI as DE98000818

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  • Annual meeting of the Society of Photo-Optical Instrumentation Engineers, San Diego, CA (United States), 27 Jul - 1 Aug 1997

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  • Other: DE98000818
  • Report No.: LA-UR--97-2702
  • Report No.: CONF-970706--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 627360
  • Archival Resource Key: ark:/67531/metadc689653

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  • July 1, 1997

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  • Aug. 14, 2015, 8:43 a.m.

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  • Feb. 25, 2016, 2:13 p.m.

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Theiler, J. & Gisler, G. A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation, article, July 1, 1997; New Mexico. (digital.library.unt.edu/ark:/67531/metadc689653/: accessed November 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.