DocumentCode
1563201
Title
Novel Features for Polarimetric SAR Image Classification by Neural Network
Author
Khan, Kamran Ullah ; Yang, Jian
Author_Institution
Dept. of Electron. Eng., Tsinghua Univ., Beijing
Volume
1
fYear
2005
Firstpage
165
Lastpage
170
Abstract
This paper presents a set of effective features derived from the coherence matrix of polarimetric SAR data. Neural network is used as the classification engine. The maximum likelihood estimator (MLE) result is used as the reference to compare the result of the proposed method. It is demonstrated that the average classification accuracy by the proposed method is more than that by the MLE. The maximum overall efficiency obtained by the proposed method is 95.4%
Keywords
image classification; matrix algebra; maximum likelihood estimation; neural nets; radar imaging; synthetic aperture radar; coherence matrix; maximum likelihood estimation; neural network; polarimetric SAR image classification; Data engineering; Discrete wavelet transforms; Electronic mail; Engines; Frequency; Image classification; Low pass filters; Maximum likelihood estimation; Neural networks; Principal component analysis; Neural Network; Undecimated discrete wavelet transform (UDWT); principal component analysis (PCA);
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9422-4
Type
conf
DOI
10.1109/ICNNB.2005.1614590
Filename
1614590
Link To Document