AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES

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The authors examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. They present AFREET: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that AFREET achieves significantly higher performance. The use of spatial context … continued below

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PERKINS, S. & HARVEY, N. February 1, 2001.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by the UNT Libraries Government Documents Department to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 22 times. More information about this article can be viewed below.

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The authors examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. They present AFREET: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that AFREET achieves significantly higher performance. The use of spatial context is particularly useful for medical imagery, where multispectral images are still rare.

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606 Kilobytes pages

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  • Report No.: LA-UR-01-891
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 774619
  • Archival Resource Key: ark:/67531/metadc717515

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  • February 1, 2001

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  • Sept. 29, 2015, 5:31 a.m.

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  • March 24, 2016, 5:05 p.m.

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PERKINS, S. & HARVEY, N. AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES, article, February 1, 2001; New Mexico. (https://digital.library.unt.edu/ark:/67531/metadc717515/: accessed April 24, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.

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