High spectral resolution of hyperspectral images comes hand in hand with high data redundancy (i.e. multiple bands carrying similar information), which further contributes to high computational costs, complexity and data storage. Hence, in this work, we aim at performing dimensionality reduction by selection of non-redundant bands from hyperspectral image of Indian Pines using spectral clustering. We represent the dataset in the form of similarity graphs computed from metrics such as Euclidean, and Tanimoto Similarity using K-Nearest neighbor method. The optimum k for our dataset is identified using methods like Distribution Compactness (DC) algorithm, elbow plot, histogram and visual inspection of the similarity graphs. These methods give us a range for the optimum value of k. The exact value of clusters k is estimated using Silhouette, Calinski-Harbasz, Dunn’s and Davies-Bouldin Index. The value indicated by majority of indices is chosen as value of k. Finally, we have selected the bands closest to the centroids of the clusters, computed by using K-means algorithm. Tanimoto similarity suggests 17 bands out of 220 bands, whereas the Euclidean metric suggests 15 bands for the same. The accuracy of classified image before band selection using support vector machine (SVM) classifier is 76.94% and after band selection is 75.21% & 75.56% for Tanimoto and Euclidean matrices respectively.