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Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationship of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral image (HSI) that possesses many homogenous areas. In this paper,...
This paper presented a new method of lithological mapping using extended one-class kernel sparse representation, a new one-class classifier. In the proposed method, to address spectral variability of lithological types, learning vector quantization for novelty detection was adopted to produce several clusters before the classification process. The one-class kernel sparse representation was adopted...
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