 |
|
|
|
IEEE publications: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases,these works may not be reposted without the explicit permission of the copyright holder.
J. Li and J. Bioucas-Dias, "Minimum volume simplex analysis: a fast algorithm to unmix hyperspectral data ", in IEEE International Geoscience and Remote sensing Symposium IGARSS’2008, Boston, USA, 2008.
Abstract: This paper presents a new method of minimum volume class for hyperspectral unmixing, termed minimum volume simplex analysis (MVSA). The underlying mixing model is linear; i.e., the mixed hyperspectral vectors are modeled by a linear mixture of the endmember signatures weighted by the correspondent abundance fractions. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem is solved by implementing a sequence of quadratically constrained subproblems. In a final step, the hard constraint on the abundance fractions is replaced with a hinge-type loss function to account for outliers and noise. We illustrate the state-of-the-art performance of the MVSA algorithm in unmixing simulated data sets. We are mainly concerned with the realistic scenario in which the pure pixel assumption (i.e., there exists at least one pure pixel per endmember) is not fulfilled. In these conditions, the MVSA yields much better performance than the pure pixel based algorithms.
J. Li, J. Bioucas-Dias and A. Plaza. Hyperspectral Image Segmentation Using a New Bayesian Approach with Active Learning. IEEE Transactions on Geoscience and Remote Sensing. vol.49, no.10, pp.3947-3960, Oct. 2011.
Abstract: This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation, with two main steps: (a) learning, for each class label, the posterior probability distributions, based on a multinomial logistic regression model; (b) segmenting the hyperspectral image, based on the posterior probability distribution learnt in step (a) and on a multi-level logistic prior encoding the spatial information. The multinomial logistic regressors are learnt by using the recently introduced LORSAL (logistic regression via splitting and augmented Lagrangian) algorithm. The maximum a posterior segmentation is efficientlycomputed by the α-Expansion min-cut based integer optimization algorithm. Aiming at reducing the costs of acquiring large training sets, active learning is performed using a mutual information based criterion. State-of-the-art performance of the proposed approach is illustrated with simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral classification methods.