Hyperspectral Image Classification Based on Relaxed Clustering Assumption and Spatial Laplace Regularizer

In this letter, a relaxed clustering assumption and spatial Laplace-regularizer-based semisupervised hyperspectral image classifier is proposed. Considering the mixed pixels and noise intrinsic in hyperspectral image, we relax the clustering assumption employed in most of the available classifiers s...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2014-05, Vol.11 (5), p.901-905
Hauptverfasser:	Yang, Shuyuan, Qiao, Yu, Yang, Lixia, Jin, Penglei, Jiao, Licheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Alternating optimization Hyperspectral imaging Laplace regularizer Optimization relaxed clustering assumption spatial constraint Support vector machines Vectors
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