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Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data
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DOI：10.1109/LGRS.2009.2024624.png)
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In real applications, it is difficult to obtain a sufficient number of training samples in supervised classification of hyperspectral remote sensing images. Furthermore, the training samples may not represent the real distribution of the whole space. To attack these problems, an ensemble algorithm which combines generative (mixture of Gaussians) and discriminative (support cluster machine) models for classification is proposed. Experimental results carried out on hyperspectral data set collected by the reflective optics system imaging spectrometer sensor, validates the effectiveness of the proposed approach.
Keyword:
Ensemble classification
hyperspectral remote sensing images
mixture of Gaussians (MoGs)
support cluster machine (SCM)
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16.4
论文数:
1.0W
被引数:
5.1K
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Semisupervised classification of hyperspectral images by SVMs optimized in the primal基于原始优化svm的高光谱图像半监督分类
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