An Unsupervised Spectral Matching Classifier Based on Artificial DNA Computing for Hyperspectral Remote Sensing Imagery

Hyperspectral remote sensing image clustering, with the large volume, high dimensions, and temporal-spatial spectral diversity, is a challenging task due to finding interesting clusters in the sparse feature space. In this paper, a novel hyperspectral clustering algorithm, namely, an unsupervised spectral matching classifier based on artificial DNA computing (UADSM), is proposed to perform the task of clustering different ground objects in specific spectral DNA feature encoding subspaces. UADSM builds up the clustering framework with the spectral encoding, optimizing, and matching mechanism by introducing the basic notions and operators of artificial DNA computing. By discretized spectral DNA feature encoding processing, the spectral shape, amplitude, and slope features of the hyperspectral data are extracted. Furthermore, the optimal clustering centers in the form of DNA strands can be found by recombining the DNA strands in the spectral DNA encoding subspace. Finally, a reasonable category for each spectral signature is automatically identified by the normalized spectral DNA similarity norm. The traditional clustering methods of k-means, ISODATA, fuzzy c-means classifier, and FCM and MoDEFC after principal component analysis transformation are provided to compare with the UADSM classifier, using Hyperspectral Digital Imagery Collection Experiment and Reflective Optics System Imaging Spectrometer hyperspectral images. The experimental results show that the UADSM classifier can achieve the best classification accuracy; hence, it is considered that the UADSM classifier is an effective clustering method for hyperspectral remote sensing imagery.