Informative Vector Machine Classification for Hyperspectral Imagery

doi:10.11947/j.AGCS.2015.20140600

Abstract

Abstract: Informative vector machine is a method of sparse Gaussian process based on Bayesian theory, which has high speed in model training, small consuming in memory, strong effective in sparseness and good forecasting performance. In this paper, the Gaussian process regression model is introduced firstly, and then a hyperspectral imagery classification method based on informative vector machine is brought forward. Secondly, to solve the problem of non-Gaussian noise model in the Gaussian process classification, the classification problem is transformed into a regression problem by using the assume density filtering algorithm, after which model is trained by maximizing the marginal likelihood function. Finally, the number of informative vector is chosen in active subset to achieve the purpose of sparse. According to the experimental results of ROSIS images, the advantages of hyperspectral imagery classification method based on informative vector machine are validated.

Key words: hyperspectral imagery, informative vector machine, classification

CLC Number:

P237

TAN Xiong, YU Xuchu, QIN Zhiyuan, ZHANG Pengqiang, WEI Xiangpo. Informative Vector Machine Classification for Hyperspectral Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11): 1227-1234.

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