A New Spectral-Spatial Subspace Clustering Algorithm For Hyperspectral Image Analysis

In the past decade, hyperspectral imaging techniques have been widely used in various applications to acquire high spectral-spatialresolution images from different objects and materials. Although hyperspectral images (HSIs) are useful tools to obtain valuableinformation from different materials, the processing of such data is challenging due to several reasons such as the high dimension-ality and redundancy of the feature space. Therefore, advanced machine learning algorithms have been developed to analyse HSIs.Among the developed algorithms, unsupervised learning techniques have become popular since they are capable of processing HSIswithout having prior knowledge. Generally, unsupervised learning algorithms analyse HSIs based on spectral information. How-ever, in many applications, spatial information plays an eminent role, in particular when the input data is of high spatial resolution.In this study, we propose a new clustering approach by utilizing the sparse subspace-based concept within the hidden Markov ran-dom field (HMRF) structure to process HSIs in an unsupervised manner. The qualitative analyses of the obtained clustering resultsshow that the proposed spectral-spatial clustering algorithm outperforms the sparse subspace-based clustering algorithm that onlyuses spectral information.