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
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);
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614590
