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Unsupervised Image Segmentation based on Texems for Hyperspectral data

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There is no doubt about how useful and valuable the information provided by the hyperspectral sensors can be. Image segmentation procedures can take advantage of this information to increase the ability for separating different textures in an image. A multiscale approach for segmenting hyperspectral images is presented in this work. The method is based on the recently proposed texem model which has been extended in this work to spaces of high dimensionality. Furthermore, the hyperspectral extension of the texem-based segmentation would be computationally impracticable without a prior step for reducing the dimensionality. Thus, a band selection process based on the mutual information among bands has also been applied. The complete process is particularly useful in applications for remote sensing or quality inspection tasks.
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Document Type: Research Article

Publication date: January 1, 2008

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  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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