Hyperspectral Image Classification: A k-means Clustering Based Approach

This paper proposes a new scheme for hyperspectral image classification through k-means clustering. The scheme includes three steps. Firstly, principal component analysis (PCA) is utilized for dimension reduction of the hyperspectral image. Secondly, the reduced features are clustered using k-means clustering algorithm and subsequently the clusters are trained separately by multi-class support vector machine (M-SVM). Three benchmark images have been used to validate the proposed method. The suggested method is compared with a standard technique, called PCA + M-SVM and it is observed that the proposed scheme gives better results in terms of classification accuracy and execution time.