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

PDF Version Also Available for Download.

Description

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

Physical Description

606 Kilobytes pages

Creation Information

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

Context

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

Who

People and organizations associated with either the creation of this article or its content.

Sponsor

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

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.

Physical Description

606 Kilobytes pages

Source

  • Conference title not supplied, Conference location not supplied, Conference dates not supplied

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • 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

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • February 1, 2001

Added to The UNT Digital Library

  • Sept. 29, 2015, 5:31 a.m.

Description Last Updated

  • March 24, 2016, 5:05 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 1
Total Uses: 8

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

PERKINS, S. & HARVEY, N. AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES, article, February 1, 2001; New Mexico. (digital.library.unt.edu/ark:/67531/metadc717515/: accessed September 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.