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Dimensionality Reduction with Weighted K-Means for Hyperspectral Image Classification

Classification of remotely sensed images is a challenging task due to their inherently high dimensionality. Conventional methods to combat this issue involves using feature selection or extraction before feeding data to a discriminator. An intuitive approach known as Automatic Variable Weighting K-Means (W-K-means) incorporates the use of learned feature weights to the K - Means clustering algorithm to place emphasis on more prominent features. The inclusion of feature weights assists in the discovery of optimal cluster centers, thus increasing classification accuracy. As W-K-means was proposed for high-dimensional data, it is possible to achieve excellent hyperspectral image segmentation. However, the effectiveness in a high-dimensional setting was not thoroughly explored as the original experiments used datasets of low to medium dimensions. By combining feature extraction with W-K-means, essential features can be used to influence clustering. The experimental results show that Principal Component Analysis (PCA) with W-K-means performs exceptionally well in high-dimensional space when compared to W-K-means solely.