Abstract : Classification of remotely sensed data is an important task for many practical applications. However, it is not always possible to get the ground truth for supervised learning methods. Thus unsupervised methods form a valuable tool in such situations. Such methods are referred to as clustering methods. There exists several strategies for clustering the given data-K-means, density based methods, spectral clustering etc. Recently we proposed a novel method for clustering data-Power Spectral Clustering. In this article we aim to introduce the method in the context of Geoscience and Remote Sensing, apply the method to hyperspectral data and validate its applicability to remotely sensed images.