EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Pai-Hui Hsu¹ 

[1] Dept. of Civil Engineering, National Taiwan University, No.1, Sec. 4, Roosevelt Rd., Taipei City 10617, Taiwan

关键词: Hyperspectral;    Classification;    Wavelet;    Neural Networks;    Wavelet Networks;    Initialization;   

DOI  :  10.5194/isprs-archives-XLI-B7-83-2016

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.

【 授权许可】

CC BY