In this thesis, 2D Local Discriminant Bases (LDB) algorithm is used to 2D search structure to classify remotely sensed data. 2D Linear Discriminant Analysis (LDA) method is converted into an M-ary classifier by combining majority voting principle and linear distance parameters. The feature extraction algorithm extracts the relevant features by removing the irrelevant ones and/or combining the ones which do not represent supplemental information on their own. The algorithm is implemented on a remotely sensed airborne data set from Tippecanoe County, Indiana to evaluate its performance. The spectral and spatial-frequency features are extracted from the multispectral data and used for classifying vegetative species like corn, soybeans, red clover, wheat and oat in the data set.
Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12610782/index.pdf |
Date | 01 August 2009 |
Creators | Tekinay, Cagri |
Contributors | Yardimci Cetin, Yasemin |
Publisher | METU |
Source Sets | Middle East Technical Univ. |
Language | English |
Detected Language | English |
Type | M.S. Thesis |
Format | text/pdf |
Rights | Access forbidden for 1 year |
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