Cluster-based feature extraction and data fusion in the wavelet domain

This paper concentrates on a linear feature extraction method for neural network classifiers. The considered feature extraction method is based on discrete wavelet transformations (DWTs) and a cluster-based procedure, i.e., cluster-based feature extraction of the wavelet coefficients of remote sensing and geographic data is considered. The cluster-based feature extraction is a preprocessing routine that computes feature-vectors to group the wavelet coefficients in an unsupervised way. These feature-vectors are then used as a mask or a filter for the selection of representative wavelet coefficients that are used to train the neural network classifiers. In experiments, the proposed feature extraction methods performed well in neural networks classifications of multisource remote sensing and geographic data.