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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Vatsavai, R.R. Badhuri, B. Shekhar, S. Burk, T.E. |
| Copyright Year | 2008 |
| Description | Author affiliation: Comput. Sci. & Eng. Div., Oak Ridge Nat. Lab., Oak Ridge, TN (Vatsavai, R.R.; Badhuri, B.) || Remote Sensing Lab., Univ. of Minnesota, Minneapolis, MN (Burk, T.E.) || Dept. of Comput. Sci., Univ. of Minnesota, Minneapolis, MN (Shekhar, S.) |
| Abstract | In many practical situations thematic classes can not be discriminated by spectral measurements alone. Often one needs additional features such as population density, road density, wetlands, elevation, soil types, etc. which are discrete attributes. On the other hand remote sensing image features are continuous attributes. Finding a suitable statistical model and estimation of parameters is a challenging task in multisource (e.g., discrete and continuous attributes) data classification. In this paper we present a semi-supervised learning method by assuming that the samples were generated by a mixture model, where each component could be either a continuous or discrete distribution. Overall classification accuracy of the proposed method is improved by 12% in our initial experiments. |
| File Size | 146525 |
| File Format | |
| ISBN | 9781424428076 |
| DOI | 10.1109/IGARSS.2008.4779525 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-07-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Semisupervised learning Remote sensing Parameter estimation Soil Maximum likelihood estimation Supervised learning Error analysis Information science Data engineering Laboratories multisource data Semi-supervised learning expectation maximization GMM |
| Content Type | Text |
| Resource Type | Article |
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