A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination

[1]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[2]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[3]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[4]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[5]  T. Wilson,et al.  Hyperspectral remote sensing technology (HRST) program , 1998, 1998 IEEE Aerospace Conference Proceedings (Cat. No.98TH8339).

[6]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[7]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[8]  Jiang Li,et al.  Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[9]  Wesley E. Snyder,et al.  Band selection using independent component analysis for hyperspectral image processing , 2003, 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings..

[10]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[11]  Lawrence O. Hall,et al.  Active learning to recognize multiple types of plankton , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[12]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[13]  Lishuang Li,et al.  Extracting location names from Chinese texts based on SVM and KNN , 2005, 2005 International Conference on Natural Language Processing and Knowledge Engineering.

[14]  Martial Hebert,et al.  Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[15]  Xiaojin Zhu,et al.  Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning , 2005, ICML.

[16]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Liangpei Zhang,et al.  An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery , 2006, IEEE Trans. Geosci. Remote. Sens..

[19]  Gustavo Camps-Valls,et al.  Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.

[20]  Kun Tan,et al.  HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON SUPPORT VECTOR MACHINE: HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON SUPPORT VECTOR MACHINE , 2008 .

[21]  Hai Jin,et al.  MSVM-kNN: Combining SVM and k-NN for Multi-class Text Classification , 2008, IEEE International Workshop on Semantic Computing and Systems.

[22]  José Luis Rojo-Álvarez,et al.  Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Alessandro Neri,et al.  Imaging Spectroscopy , 2009, Encyclopedia of Biometrics.

[24]  William J. Emery,et al.  Active Learning Methods for Remote Sensing Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Tao Mei,et al.  Graph-based semi-supervised learning with multiple labels , 2009, J. Vis. Commun. Image Represent..

[26]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[27]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Melba M. Crawford,et al.  Locally consistent graph regularization based active learning for hyperspectral image classification , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[29]  Jon Atli Benediktsson,et al.  Segmentation and Classification of Hyperspectral Images Using Minimum Spanning Forest Grown From Automatically Selected Markers , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Melba M. Crawford,et al.  Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[32]  Wei Zhang,et al.  Multiple Classifier System for Remote Sensing Image Classification: A Review , 2012, Sensors.

[33]  Melba M. Crawford,et al.  View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[34]  The Application Research of Hyperspectral Remote Sensing Technology in Tailing Mine Environment Pollution Supervise Management , 2012, 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering.

[35]  Liangpei Zhang,et al.  An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Michael T. Eismann,et al.  Hyperspectral Image Classification , 2012 .

[37]  Liangpei Zhang,et al.  Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[38]  J. Benediktsson,et al.  Semi-Supervised Self Learning for Hyperspectral Image Classification , 2012 .

[39]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[40]  Raviv Raich,et al.  A generative semi-supervised model for multi-view learning when some views are label-free , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[41]  Jon Atli Benediktsson,et al.  Semisupervised Self-Learning for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Shaohui Mei,et al.  Hyperspectral image classification based on iterative Support Vector Machine by integrating spatial-spectral information , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[43]  Jon Atli Benediktsson,et al.  Generalized Composite Kernel Framework for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Ying Wang,et al.  Spatial–Spectral Information-Based Semisupervised Classification Algorithm for Hyperspectral Imagery , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Jun Li,et al.  ${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Lorenzo Bruzzone,et al.  An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning , 2014, IEEE Geoscience and Remote Sensing Letters.

[47]  Qian Du,et al.  An efficient semi-supervised classification approach for hyperspectral imagery , 2014 .

[48]  Xiaojin Zhu,et al.  Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.