Classification of hyperspectral imagery using a fully complex-valued wavelet neural network with deep convolutional features

Abstract

The number of spectral bands obtained by hyperspectral sensors improves the ability to distinguish physical objects and materials. But it also brings new challenges to image classification and analysis. In this study, a novel deep learning-based hybrid model called CNN-CVWNN is presented for the hyperspectral images classification (HSIs). The model uses a convolutional neural network (CNN) to extract multilayer image representation and uses the complex valued wavelet neural network (CVWNN) to classify the image using extracted features. The process steps of the proposed method are briefly as follows. First of all, the CNN algorithm has been applied to hyperspectral images. After this stage, efficient features have been obtained. These extracted features were then converted into a complex-valued number format using a novel random based transformation method. Thus, a novel complex-valued attribute set has been obtained for the HSI classification. The obtained features have been presented as input to the CVWNN algorithm. The hybrid method replaces real valued neural network inside CNN with CVWNN to enhance robustness and generalization of CNN. The experiments have been carried out on three data sets consisted of three popular hyperspectral airborne images. The developed method increases classification accuracy compared to other classification approaches.