Classification of hyperspectral remote sensing images with support vector machines
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[1] Lorenzo Bruzzone,et al. Support vector machines for classification of hyperspectral remote-sensing images , 2002, IEEE International Geoscience and Remote Sensing Symposium.
[2] Jonathan Robinson,et al. Combining support vector machine learning with the discrete cosine transform in image compression , 2003, IEEE Trans. Neural Networks.
[3] Massimiliano Pontil,et al. Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[4] Joachim M. Buhmann,et al. Support vector machines for land usage classification in Landsat TM imagery , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).
[5] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[6] Philip H. Swain,et al. Purdue e-Pubs , 2022 .
[7] John A. Richards,et al. Cluster-space representation for hyperspectral data classification , 2002, IEEE Trans. Geosci. Remote. Sens..
[8] J. Kittler,et al. Feature Set Search Alborithms , 1978 .
[9] David A. Landgrebe,et al. Hyperspectral data analysis and supervised feature reduction via projection pursuit , 1999, IEEE Trans. Geosci. Remote. Sens..
[10] Sebastiano B. Serpico,et al. Multisource data classification with dependence trees , 2002, IEEE Trans. Geosci. Remote. Sens..
[11] Pavel Pudil,et al. Introduction to Statistical Pattern Recognition , 2006 .
[12] Joydeep Ghosh,et al. Best-bases feature extraction algorithms for classification of hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..
[13] Joydeep Ghosh,et al. Adaptive Feature Spaces For Land Cover Classification With Limited Ground Truth Data , 2004, Int. J. Pattern Recognit. Artif. Intell..
[14] IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 34. NO. 4, JULY 1996 Universal Multifractal Scaling of Synthetic , 1996 .
[15] Chih-Jen Lin,et al. Radius Margin Bounds for Support Vector Machines with the RBF Kernel , 2002, Neural Computation.
[16] Ulrich H.-G. Kreßel,et al. Pairwise classification and support vector machines , 1999 .
[17] David A. Landgrebe,et al. Hierarchical classifier design in high-dimensional numerous class cases , 1991, IEEE Trans. Geosci. Remote. Sens..
[18] J. Anthony Gualtieri,et al. Support vector machines for hyperspectral remote sensing classification , 1999, Other Conferences.
[19] David A. Landgrebe,et al. Covariance Matrix Estimation and Classification With Limited Training Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[20] John A. Richards,et al. Remote Sensing Digital Image Analysis , 1986 .
[21] Lorenzo Bruzzone,et al. An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection , 1995, IEEE Trans. Geosci. Remote. Sens..
[22] Lorenzo Bruzzone,et al. A new search algorithm for feature selection in hyperspectral remote sensing images , 2001, IEEE Trans. Geosci. Remote. Sens..
[23] Maurice G. Kendall,et al. A Course in the Geometry of n Dimensions , 1962 .
[24] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[25] J. A. Gualtieri,et al. Support vector machines for classification of hyperspectral data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).
[26] James A. Bucklew,et al. Support vector machines and the multiple hypothesis test problem , 2001, IEEE Trans. Signal Process..
[27] David A. Landgrebe,et al. Feature Extraction Based on Decision Boundaries , 1993, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Qiong Jackson,et al. An adaptive classifier design for high-dimensional data analysis with a limited training data set , 2001, IEEE Trans. Geosci. Remote. Sens..
[29] Fabio Roli,et al. Support vector machines for remote sensing image classification , 2001, SPIE Remote Sensing.
[30] Isabelle Guyon,et al. Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).
[31] William Philpot,et al. A derivative-aided hyperspectral image analysis system for land-cover classification , 2002, IEEE Trans. Geosci. Remote. Sens..
[32] G. F. Hughes,et al. On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.
[33] Lorenzo Bruzzone,et al. A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images , 1999, IEEE Trans. Geosci. Remote. Sens..
[34] David A. Landgrebe,et al. HYPERSPECTRAL DATA ANALYSIS AND FEATURE REDUCTION VIA PROJECTION PURSUIT , 1999 .
[35] 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..
[36] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[37] Nikolas P. Galatsanos,et al. A support vector machine approach for detection of microcalcifications , 2002, IEEE Transactions on Medical Imaging.
[38] Keinosuke Fukunaga,et al. Introduction to statistical pattern recognition (2nd ed.) , 1990 .
[39] David A. Landgrebe,et al. Classification of remote sensing images having high spectral resolution , 1996 .
[41] David A. Landgrebe,et al. Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data , 1998, IEEE Trans. Syst. Man Cybern. Part C.
[42] L. S. Davis,et al. An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .
[43] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[44] David A. Landgrebe,et al. Covariance estimation with limited training samples , 1999, IEEE Trans. Geosci. Remote. Sens..
[45] Jon Atli Benediktsson,et al. Classification of multisource and hyperspectral data based on decision fusion , 1999, IEEE Trans. Geosci. Remote. Sens..
[46] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[47] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[48] Robert F. Cromp,et al. Support Vector Machine Classifiers as Applied to AVIRIS Data , 1999 .