National Technical Reports Library

The National Technical Information Service acquires, indexes, abstracts, and archives the largest collection of U.S. government-sponsored technical reports in existence. The NTRL offers online, free and open access to these authenticated government technical reports. Technical reports and documents in its repository may be available online for free either from the issuing federal agency, the U.S. Government Publishing Office’s Federal Digital System website, or through search engines.

Details

DE2001774619

Publication Date	2001
Personal Author	Perkins, S.; Harvey, N.
Page Count	9
Abstract	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.
Keywords	Performance Images Classification Construction Learning Remote sensing Vectors
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	72F - Statistical Analysis
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200125
Contract Number	W-7405-ENG-36

Afreet: human-inspired spatio-spectral feature construction for image classification with support vector machines.

Afreet: human-inspired spatio-spectral feature construction for image classification with support vector machines.
DE2001774619

Publication Date	2001
Personal Author	Perkins, S.; Harvey, N.
Page Count	9
Abstract	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.
Keywords	Performance Images Classification Construction Learning Remote sensing Vectors
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	72F - Statistical Analysis
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200125
Contract Number	W-7405-ENG-36