DU Peijun, XIA Junshi, XUE Zhaohui, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016,20(2):236-256.
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.
关键词
高光谱遥感分类支持向量机特征挖掘多分类器集成
Keywords
hyperspectral remote sensingclassificationsupport vector machinefeature miningmultiple classifier system