Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

Abstract

This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and highly mixed pixels. Then, contextual information is included using a multilevel logistic Markov–Gibbs Markov random field prior. Finally, a maximum a posteriori segmentation is efficiently computed by the α-Expansion mincut-based integer optimization algorithm. The proposed segmentation approach is experimentally evaluated using both simulated and real hyperspectral data sets, exhibiting state-of-the-art performance when compared with recently introduced hyperspectral image classification methods. The integration of subspace projection methods with the MLR algorithm, combined with the use of spatial–contextual information, represents an innovative contribution in the literature. This approach is shown to provide accurate characterization of hyperspectral imagery in both the spectral and the spatial domain.

DOI: 10.1109/TGRS.2011.2162649

Extracted Key Phrases

0501002011201220132014201520162017
Citations per Year

266 Citations

Semantic Scholar estimates that this publication has 266 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Li2012SpectralSpatialHI, title={Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields}, author={Jun Li and Jos{\'e} M. Bioucas-Dias and Antonio J. Plaza}, journal={IEEE Trans. Geoscience and Remote Sensing}, year={2012}, volume={50}, pages={809-823} }