| Bibliographic Citation | |
| Full Text |  664 K |
|---|---|
| Title | SUPPORT VECTOR MACHINES FOR BROAD AREA FEATURE CLASSIFICATION IN REMOTELY SENSED IMAGES |
| Creator/Author | S. PERKINS ; N. HARVEY ; ET AL |
| Publication Date | 2001 Mar 01 |
| OSTI Identifier | OSTI ID: 776688 |
| Report Number(s) | LA-UR-01-1689 |
| DOE Contract Number | W-7405-ENG-36 |
| Other Number(s) | TRN: AH200121%%105 |
| Resource Type | Conference |
| Resource Relation | Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Mar 2001 |
| Research Org | Los Alamos National Lab., NM (US) |
| Sponsoring Org | US Department of Energy (US) |
| Subject | 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; CLASSIFICATION; LEARNING; NEURAL NETWORKS; REMOTE SENSING; SATELLITES; STATISTICS; TRAINING; VECTORS; IMAGES |
| Description/Abstract | Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include maximum likelihood classifiers, neural networks and genetic algorithms. We present a new system called Afreet, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabilistic guarantee on how well the classifier will generalize to unseen data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hype spectral data), and scales well to large problem sizes. Afreet combines an SVM with a sophisticated spatio-spectral feature construction mechanism that allows it to classify spectrally ambiguous pixels. We demonstrate the effectiveness of the system by applying Afreet to several broad area classification problems in remote sensing, and provide a comparison with conventional maximum likelihood classification. |
| Country of Publication | United States |
| Language | English |
| Format | Medium: ED; Size: 665 Kilobytes pages |
| System Entry Date | 2008 Feb 05 |
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Last Updated: 03/15/2009