Classification Of Remotely Sensed Data By Using 2d Local Discriminant Bases

• Main Author: Tekinay, Cagri
• Other Authors: Yardimci Cetin, Yasemin
• Format: Others
• Language: Eng
• Published: METU 2009
• Subjects: QA General 15707; Remote Sensing Local Discriminant Bases Linear Discriminant Analysis Hyperspectral Imaging M-ary Classification
• Online Access: http://etd.lib.metu.edu.tr/upload/3/12610782/index.pdf

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.