1. J. Li, J. M. Bioucas-Dias, A. Plaza and L. Liu. Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, % pp. 6076-6090, Oct. 2016. doi: 10.1109/TGRS.2016.2580702. (Download Matlab Demo)
    2. Abstract: Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. It amounts to identifying a set of pure spectral signatures, which are called endmembers, and their corresponding fractional, draftrulesabundances in each pixel of the hyperspectral image. Over the last years, different algorithms have been developed for each of the three main steps of the spectral unmixing chain: 1) estimation of the number of endmembers in a scene; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene. However, few algorithms can perform all the stages involved in the hyperspectral unmixing process. Such algorithms are highly desirable to avoid the propagation of errors within the chain. In this paper, we develop a new algorithm, which is termed robust collaborative nonnegative matrix factorization (R-CoNMF), that can perform the three steps of the hyperspectral unmixing chain. In comparison with other conventional methods, R-CoNMF starts with an overestimated number of endmembers and removes the redundant endmembers by means of collaborative regularization. Our experimental results indicate that the proposed method provides better or competitive performance when compared with other widely used methods.
  2. J. Li, M. Khodadadzadeh, A. Plaza, X. Jia and J. M. Bioucas-Dias. A Discontinuity Preserving Relaxation Scheme for Spectral-Spatial Hyperspectral Image Classification.. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 625-639, February 2016. (Download Matlab Demo)
    3. Abstract: In remote sensing image processing, relaxation is defined as a method that uses the local relationship among neighboring pixels to correct spectral or spatial distortions. In recent years, relaxation methods have shown great success in classification of remotely sensed data. Relaxation, as a preprocessing step, can reduce noise and improve the class separability in the spectral domain. On the other hand, relaxation (as a postprocessing approach) works on the label image or class probabilities obtained from pixelwise classifiers. In this work, we develop a discontinuity preserving relaxation strategy, which can be used for postprocessing of class probability estimates, as well as preprocessing of the original hyperspectral image. The newly proposed method is an iterative relaxation procedure, which exploits spatial information in such a way that it considers discontinuities existing in the data cube. Our experimental results indicate that the proposed methodology leads to state-of-the-art classification results when combined with probabilistic classifiers for several widely used hyperspectral data sets, even when very limited training samples are available.
  3. J. Li, A. Agathos, D. Zaharie, J. M. Bioucas Dias, A. Plaza, and X. Li, Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 9, pp. 5067-5082, September 2015 (Download Matlab Demo)
    4. Abstract: Linear spectral unmixing aims at estimating the number of pure spectral substances, also called endmembers, their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation. 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, which is computationally complex, is solved in this paper by implementing a sequence of quadratically constrained subproblems using the interior point method, which is particularly effective from the computational viewpoint. The proposed implementation (available online: www.lx.it.pt/%7ejun/DemoMVSA.zip) is shown to exhibit state-of-the-art performance not only in terms of unmixing accuracy, particularly in nonpure pixel scenarios, but also in terms of computational performance. Our experiments have been conducted using both synthetic and real data sets. An important assumption of MVSA is that pure pixels may not be present in the hyperspectral data, thus addressing a common situation in real scenarios which are often dominated by highly mixed pixels. In our experiments, we observe that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Our results also demonstrate that MVSA is well suited to problems involving a high number of endmembers (i.e., complex scenes) and also for problems involving a high number of pixels (i.e., large scenes).
  4. J. Li, X. Huang, P. Gamba, J. Bioucas, L. Zhang, J. Benediksson, and A. Plaza. Multiple Feature Learning for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, vol.53, no.3, pp.1592-1606, March 2015. Matlab Demo Available.. Codes for Attribute Profiles. The error in the previous demo is corrected! Please read ReadMe Before using this demo, which is under the terms and policies by the Original Authors..
    5. Abstract: Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few approaches have been designed to integrate multiple types of features extracted from both linear and nonlinear transformations. In this paper, we develop a new framework for the classification of hyperspectral scenes that pursues the combination of multiple features. The ultimate goal of the proposed framework is to be able to cope with linear and nonlinear class boundaries present in the data, thus following the two main mixing models considered for hyperspectral data interpretation. An important characteristic of the presented approach is that it does not require any regularization parameters to control the weights of considered features so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a variety of input features and hyperspectral scenes, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results without significantly increasing computational complexity.
  5. J. Li, P. R. Marpu, A. Plaza, J. M. Bioucas-Dias and J. A. Benediktsson. Generalized Composite Kernel Framework for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 9, pp. 4816-4829, 2013. New Demo for Generalized Composite Kernel framework using Multinomial logistic Regression (MLR GCK) and Composite Kernel with Support Vector Machines (SVM CK) are available.(DEMO MLR-GCK and SVM-CK, Matlab Codes)
    6. Abstract: This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios.
  6. J. Li J. M. Bioucas-Dias and A. Plaza. Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 2, pp. 844-856, 2013. (Updated Matlab Code NEW DEMO for Belief Propagation, Spectral Spatial Active Learning)
    7. Abstract: In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.
  7. M. Khodadadzadeh, J. Li, A. Plaza, H. Ghassemian, J. M. Bioucas-Dias and X. Li. Spectral-Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization. IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 10, pp. 6298-6314, October 2014. (Download Matlab Demo. matlab code for windows)
    8. 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 min-cut-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.
  8. J. Li J. Bioucas-Dias and A. Plaza. Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields. IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 3, pp. 809-823, 2012. (NEW Demo for MLRsubMLL matlab code for windows)
    9. 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 min-cut-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.
  9. 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. ( matlab code for windows)
    10. Abstract: This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps. First, we use a multinomial logistic regression (MLR) model to learn the class posterior probability distributions. This is done by using a recently introduced logistic regression via splitting and augmented Lagrangian algorithm. Second, we use the information acquired in the previous step to segment the hyperspectral image using a multilevel logistic prior that encodes the spatial information. In order to reduce the cost of acquiring large training sets, active learning is performed based on the MLR posterior probabilities. Another contribution of this paper is the introduction of a new active sampling approach, called modified breaking ties, which is able to provide an unbiased sampling. Furthermore, we have implemented our proposed method in an efficient way. For instance, in order to obtain the time-consuming maximum a posteriori segmentation, we use the α-expansion min-cut-based integer optimization algorithm. The state-of-the-art performance of the proposed approach is illustrated using both simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral image analysis methods.
  10. J. Li J. Bioucas-Dias and A. Plaza. Semi-Supervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression with Active Learning. IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 11, pp. 4085-4098, 2010. (matlab code for windows)
    11. Abstract: This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions and on a Markov random field. The posterior class distributions are modeled using multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. Such unlabeled samples are actively selected based on the entropy of the corresponding class label. The prior on the image of labels is a multilevel logistic model, which enforces segmentation results in which neighboring labels belong to the same class. The maximum a posteriori segmentation is computed by the α-expansion min-cut-based integer optimization algorithm. Our experimental results, conducted using synthetic and real hyperspectral image data sets collected by the Airborne Visible/Infrared Imaging Spectrometer system of the National Aeronautics and Space Administration Jet Propulsion Laboratory over the regions of Indian Pines, IN, and Salinas Valley, CA, reveal that the proposed approach can provide classification accuracies that are similar or higher than those achieved by other supervised methods for the considered scenes. Our results also indicate that the use of a spatial prior can greatly improve the final results with respect to a case in which only the learned class densities are considered, confirming the importance of jointly considering spatial and spectral information in hyperspectral image segmentation.