Studies on hyperspectral remote sensing image classification have developed rapidly with the progress of related disciplines

including pattern recognition

machine learning

and remote sensing technology. This review generates a systematic summary and conducts a comprehensive evaluation of the advancements in current techniques for hyperspectral remote sensing image classification. Based on an overview of different classification schemes

we examine the recent progress in per-pixel classification algorithms for hyperspectral images from six aspects

namely

new classifier design（e.g.

kernel-based methods）

feature mining

spectral spatial classification

active and semisupervised learning

sparse representation for classification

and multiple classifier systems. Future research directions are discussed as well.On the one hand

new theories and methods of machine learning should be introduced continuously into hyperspectral image classification.Moreover

multisource data and multidimensional feature spaces may improve the accuracy

generalization capability

and automation degree of a classifier. On the other hand

new classification methods should be designed in consideration of practical requirements to meet the needs of real applications and to emphasize the advantages of fine spectra in hyperspectral remote sensing.