Wavelet Networks for Hyperspectral Image Classification
Date Issued
2007
Date
2007
Author(s)
Yang, Hsiu-Han
DOI
en-US
Abstract
The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. The experiment results showed that the wavelet networks classifier has better classification accuracy than traditional back propagation neural networks with features from wavelet transform and from principle component analysis, and exactly is an effective tool for classification of hyperspectral images.
Subjects
小波特徵萃取
小波神經網路
維度縮減
影像分類
Hyperspecteal Images
Wavelet-based Feature Extraction
Wavelet Networks
Dimensionality Reduction
Image Classification
Type
thesis