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Classification of hyperspectral remote sensing data is more challenging than multispectral remote sensing data because of the enormous amount of information available in the many spectral bands. During the last few decades, significant efforts have been made to investigate the effectiveness of the traditional multispectral classification approaches on hyperspectral data. Formerly extensively established conventional classification methods have been dominated by the advanced classification approaches and many pre-processing techniques have been developed and incorporated in hyperspectral classification. A perspective survey of hyperspectral remote sensing classification approaches is presented here. It comprehensively highlights the taxonomy of major classification approaches reported during the last two decades and describes an experimental evaluation of a few major classification algorithms. Recent advancements in the development of classification approaches are also emphasized with a set of guidelines for achieving better classification performances.

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