Spectral-Spatial Classification of Hyperspectral Images Using Approximate Sparse Multinomial Logistic Regression

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Abstract: We propose the sparse multinomial logistic regression (SMLR) model for spectral-spatial classification of hyperspectral images. In the proposed method, the parameters of SMLR are iteratively estimated from logposterior by using Laplace approximation. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The estimated parameters are used for spectralspatial classification of hyperspectral images using a spatial prior. The experimental results on real hyperspectral images show that the classification accuracy of proposed method is also better than those of state-of-the art methods.

WSEAS Transactions on Mathematics, ISSN / E-ISSN: 1109-2769 / 2224-2880, Volume 16, 2017, Art. #7

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Koray Kayabol, "Spectral-Spatial Classification of Hyperspectral Images Using Approximate Sparse Multinomial Logistic Regression," WSEAS Transactions on Mathematics, vol. 16, pp. 57-61, 2017, DOI:
Koray Kayabol. Spectral-Spatial Classification of Hyperspectral Images Using Approximate Sparse Multinomial Logistic Regression. WSEAS Transactions on Mathematics. 2017;16:57-61.
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