The Experts below are selected from a list of 62130 Experts worldwide ranked by ideXlab platform
Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.
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Class-Specific Sparse Multiple Kernel Learning for Spectral–Spatial Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Tianzhu Liu, Jon Atli Benediktsson, Xiuping Jia, Jocelyn ChanussotAbstract:In recent years, many studies on Hyperspectral Image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of Hyperspectral Image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of Hyperspectral Images. CS-SMKL classifies the Hyperspectral Images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three Hyperspectral data sets. The experimental results show that the proposed method achieves better performances for Hyperspectral Image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features.
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Enhancing Hyperspectral Image Quality using Nonlinear PCA
2014Co-Authors: Giorgio Licciardi, Jocelyn Chanussot, Gabriel Vasile, A. PisciniAbstract:In this paper, we propose a new method aiming at reducing the noise in Hyperspectral Images. It is based on the nonlinear generalization of Principal Component Analysis (NLPCA). The NLPCA is performed by an auto associative neural network that have the Hyperspectral Image as input and is trained to reconstruct the same Image at the output. Thanks to its bottleneck structure, the AANN forces the hyper spectral Image to be projected in a lower dimensionality feature space where noise as well as both linear and nonlinear correlations between spectral bands are removed. This process permits to obtain enhancements in terms of Hyperspectral Image quality. Experiments are conducted on different real hyper spectral Images, with different contexts and resolutions. The results are qualitatively and quantitatively discussed and demonstrate the interest of the proposed method as compared to traditional approaches.
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Foreword to the Special Issue on Hyperspectral Image and Signal Processing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Alina Zare, J Bolton, Jocelyn Chanussot, Paul GaderAbstract:The seventy-four articles in this special issue present state-of-the-art algorithms and applications for Hyperspectral Image and signal processing. Algorithms address topics such as spectral unmixing, classification, target and anomaly detection, compression, data fusion, noise reduction. Applications include monitoring of vegetation and environment. The large number of papers included in this special issue is indicative of the high level of research activity, interest, and applications for Hyperspectral Image and signal analysis. The 5th Workshop on Hyperspectral Image and Signal Processing??Evolution in Remote Sensing (WHISPERS) was held on June 25??28, 2013 in Gainesville, FL, USA. WHISPERS 2013 received the technical sponsorship of the IEEE Geoscience and Remote Sensing Society (GRSS) and support from the University of Florida and the WHISPERS Foundation.
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Foreword to the special issue on Hyperspectral Image and signal processing
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Jocelyn Chanussot, Melba M. Crawford, Bor-chen KuoAbstract:ALMOST A DECADE after the milestone special issue of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) dedicated to the analysis of Hyperspectral Image data, edited by Prof. Landgrebe, Prof. Serpico, Prof. Crawford, and Prof. Singhroy [1], it is a great pleasure to introduce this new special issue on Hyperspectral Image and signal processing. In the intervening years, interest in Hyperspectral sensing has increased dramatically, as evidenced by advances in sensing technology and planning for future Hyperspectral missions, increased availability of Hyperspectral data from airborne and space-based platforms, and development of methods for analyzing data and new applications. The proposal for this special issue was also related to the launch of a series of specialized workshops on Hyperspectral sensing that had technical sponsorship of the IEEE Geoscience and Remote Sensing Society. The firstWorkshop on Hyperspectral Image and Signal Processing--Evolution in Remote Sensing (WHISPERS) was held in Grenoble, France, in 2009, with 200 attendees from 33 countries. The second was hosted in Reykjavik, Iceland, in 2010 and featured a commercial exhibition of sensors and data products, as well as an outstanding technical program. The third WHISPERS workshop is scheduled for June 2011 in Lisbon, Portugal, and will be followed by venues in Asia in 2012 and America in 2013. Following the inaugural 2009 workshop and the open call for papers, an impressive number of submissions (66) were received for this special issue, which contains 24 papers. A few of the submissions will be published in the following regular issues of TGRS, after the final reviews and revisions are completed.
Jon Atli Benediktsson - One of the best experts on this subject based on the ideXlab platform.
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Class-Specific Sparse Multiple Kernel Learning for Spectral–Spatial Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Tianzhu Liu, Jon Atli Benediktsson, Xiuping Jia, Jocelyn ChanussotAbstract:In recent years, many studies on Hyperspectral Image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of Hyperspectral Image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of Hyperspectral Images. CS-SMKL classifies the Hyperspectral Images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three Hyperspectral data sets. The experimental results show that the proposed method achieves better performances for Hyperspectral Image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features.
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spectral spatial Hyperspectral Image classification with edge preserving filtering
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Xudong Kang, Jon Atli BenediktssonAbstract:The integration of spatial context in the classification of Hyperspectral Images is known to be an effective way in improving classification accuracy. In this paper, a novel spectral-spatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the Hyperspectral Image is classified using a pixelwise classifier, e.g., the support vector machine classifier. Then, the resulting classification map is represented as multiple probability maps, and edge-preserving filtering is conducted on each probability map, with the first principal component or the first three principal components of the Hyperspectral Image serving as the gray or color guidance Image. Finally, according to the filtered probability maps, the class of each pixel is selected based on the maximum probability. Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.
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Semisupervised self-learning for Hyperspectral Image classification
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Inmaculada Dopido, Antonio J. Plaza, Prashanth Reddy Marpu, José M. Bioucas-dias, Jun Li, Jon Atli BenediktssonAbstract:Remotely sensed Hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional Images with hundreds of spectral bands. Supervised Hyperspectral Image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in Hyperspectral Image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine–machine framework when compared with traditional machine–human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. 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 use of self-learning represents an effective and pro- ising strategy in the context of Hyperspectral Image classification.
Antonio Plaza - One of the best experts on this subject based on the ideXlab platform.
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active learning with convolutional neural networks for Hyperspectral Image classification using a new bayesian approach
IEEE Transactions on Geoscience and Remote Sensing, 2018Co-Authors: Juan M Haut, Mercedes E Paoletti, Javier Plaza, Antonio PlazaAbstract:Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of Images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the Hyperspectral Image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the Hyperspectral Image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of Hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the Hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for Hyperspectral Image classification.
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spectral spatial Hyperspectral Image segmentation using subspace multinomial logistic regression and markov random fields
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Jose M Bioucasdias, Antonio PlazaAbstract: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.
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Hyperspectral Image segmentation using a new bayesian approach with active learning
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: Jose M Bioucasdias, Antonio PlazaAbstract: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.
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semisupervised Hyperspectral Image segmentation using multinomial logistic regression with active learning
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Jose M Bioucasdias, Antonio PlazaAbstract: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.
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IWANN (2) - Self-Organizing Map for Hyperspectral Image Analysis
Bio-Inspired Applications of Connectionism, 2001Co-Authors: Pablo Juan Martínez Cobo, Pedro Luis Aguilar Mateos, Rosa María Pérez Utrero, Marino Linaje Trigueros, Juan Carlos Preciado, Antonio PlazaAbstract:In this paper we present a neural network methodology used for classifying an Hyperspectral Image referencied as Indian Pines. The network Parameters (learning and neighborhood function) are adjusted using a test battery generated from the Image, selecting the values that give the best robustness and discrimination capacity. The availity of ground truth allows us to introduce a new stadistical measure to quantity the resulting classification accuracy. The results of this methodology show an accuracy of 80% in the classification.
Jose M Bioucasdias - One of the best experts on this subject based on the ideXlab platform.
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supervised Hyperspectral Image classification with rejection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Filipe Condessa, Jose M Bioucasdias, Jelena KovacevicAbstract:Hyperspectral Image classification is a challenging problem as obtaining complete and representative training sets is costly, pixels can belong to unknown classes, and it is generally an ill-posed problem. The need to achieve high classification accuracy may surpass the need to classify the entire Image. To account for this scenario, we use classification with rejection by providing the classifier with an option not to classify a pixel and consequently reject it. We present and analyze two approaches for supervised Hyperspectral Image classification that combine the use of contextual priors with classification with rejection: 1) by jointly computing context and rejection and 2) by sequentially computing context and rejection. In the joint approach, rejection is introduced as an extra class that models the probability of classifier failure. In the sequential approach, rejection results from the hidden field associated with a marginal maximum a posteriori classification of the Image. We validate both approaches on real Hyperspectral data.
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supervised Hyperspectral Image classification with rejection
International Geoscience and Remote Sensing Symposium, 2015Co-Authors: Filipe Condessa, Jose M Bioucasdias, Jelena KovacevicAbstract:Hyperspectral Image classification is a challenging classification problem: obtaining complete and representative training sets is costly; pixels can belong to unknown classes; and it is generally an ill-posed problem. The need to achieve high classification accuracy surpasses the need to classify the entire Image. To achieve this, we use classification with rejection by providing the classifier an option not to classify a pixel and consequently reject it. We propose a method for supervised Hyperspectral Image classification combining the use of contextual priors with classification with rejection. Rejection is introduced as an extra class that models the probability of classifier failure. We validate the resulting algorithm in the AVIRIS Indian Pines scene and illustrate the performance increase resulting from classification with rejection.
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robust Hyperspectral Image classification with rejection fields
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2015Co-Authors: Filipe Condessa, Jose M Bioucasdias, Jelena KovacevicAbstract:In this paper we present a novel method for robust Hyperspectral Image classification using context and rejection. Hyper-spectral Image classification is generally an ill-posed Image problem where pixels may belong to unknown classes, and obtaining representative and complete training sets is costly. Furthermore, the need for high classification accuracies is frequently greater than the need to classify the entire Image. We approach this problem with a robust classification method that combines classification with context with classification with rejection. A rejection field that will guide the rejection is derived from the classification with contextual information obtained by using the SegSALSA [1] algorithm. We validate our method in real Hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets.
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spectral spatial Hyperspectral Image segmentation using subspace multinomial logistic regression and markov random fields
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Jose M Bioucasdias, Antonio PlazaAbstract: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.
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Hyperspectral Image segmentation using a new bayesian approach with active learning
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: Jose M Bioucasdias, Antonio PlazaAbstract: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.
Huanfeng Shen - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a Hyperspectral Image limits its application and has a negative impact on Hyperspectral Image classification, unmixing, target detection, and so on. In Hyperspectral Images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a Hyperspectral Image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial Hyperspectral TV model and accelerate the speed of Hyperspectral Image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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Hyperspectral Image denoising employing a spectral spatial adaptive total variation model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a Hyperspectral Image limits its application and has a negative impact on Hyperspectral Image classification, unmixing, target detection, and so on. In Hyperspectral Images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a Hyperspectral Image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial Hyperspectral TV model and accelerate the speed of Hyperspectral Image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced.