BORDER FEATURE DETECTION AND ADAPTATION: A NEW ALGORITHM FOR CLASSIFICATION OF REMOTE SENSING IMAGES
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[1] Gustavo Camps-Valls,et al. Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.
[2] Keinosuke Fukunaga,et al. A Branch and Bound Algorithm for Computing k-Nearest Neighbors , 1975, IEEE Transactions on Computers.
[3] Ethem Alpaydm. Grow-and-Learn: An Incremental Method for Category Learning , 1990 .
[4] Okan K. Ersoy,et al. A statistical self-organizing learning system for remote sensing classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[5] Giles M. Foody,et al. The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM , 2006 .
[6] Luis O. Jimenez,et al. Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks , 1999, IEEE Trans. Geosci. Remote. Sens..
[7] R. Redner,et al. Mixture densities, maximum likelihood, and the EM algorithm , 1984 .
[8] Lorenzo Bruzzone,et al. Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[9] Chin-Liang Chang,et al. Finding Prototypes For Nearest Neighbor Classifiers , 1974, IEEE Transactions on Computers.
[10] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[11] F. Parmiggiani,et al. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.
[12] Giles M. Foody,et al. A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[13] David A. Landgrebe,et al. Covariance estimation with limited training samples , 1999, IEEE Trans. Geosci. Remote. Sens..
[14] Johannes R. Sveinsson,et al. Parallel consensual neural networks , 1997, IEEE Trans. Neural Networks.
[15] A. Belousov,et al. A flexible classification approach with optimal generalisation performance: support vector machines , 2002 .
[16] Michael T. Manry,et al. Surface parameter retrieval using fast learning neural networks , 1993 .
[17] Beng Chin Ooi,et al. BORDER: efficient computation of boundary points , 2006, IEEE Transactions on Knowledge and Data Engineering.
[18] P. Atkinson,et al. Introduction Neural networks in remote sensing , 1997 .
[19] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[20] P. K Varshney,et al. Advanced image processing techniques for remotely sensed hyperspectral data : with 128 figures and 30 tables , 2004 .
[21] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[22] David A. Landgrebe,et al. Classification of remote sensing images having high spectral resolution , 1996 .
[23] L. S. Davis,et al. An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .
[24] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[25] B. Datt,et al. On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification , 2005 .
[26] David A. Landgrebe,et al. HYPERSPECTRAL DATA ANALYSIS AND FEATURE REDUCTION VIA PROJECTION PURSUIT , 1999 .
[27] 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..
[28] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[29] F. Melgani,et al. An Adaptive SVM Nearest Neighbor Classifier for Remotely Sensed Imagery , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.
[30] 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.
[31] Teuvo Kohonen,et al. The self-organizing map , 1990 .
[32] Essaid Bouktache,et al. A Fast Algorithm for the Nearest-Neighbor Classifier , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[33] A. I. Ethem Alpaydin. Neural models of incremental supervised and unsupervised learning , 1990 .
[34] J. Sim,et al. The kappa statistic in reliability studies: use, interpretation, and sample size requirements. , 2005, Physical therapy.
[35] Joydeep Ghosh,et al. Best-bases feature extraction algorithms for classification of hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..
[36] Okan K. Ersoy,et al. A Spectral-Spatial Classification Algorithm for Multispectral Remote Sensing Data , 2003, ICANN.
[37] David A. Landgrebe,et al. Projection pursuit for high dimensional feature reduction: parallel and sequential approaches , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.
[38] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[39] Johannes R. Sveinsson,et al. Hybrid consensus theoretic classification , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.
[40] Ethem Alpaydin,et al. GAL: Networks That Grow When They Learn and Shrink When They Forget , 1994, Int. J. Pattern Recognit. Artif. Intell..
[41] David A. Landgrebe,et al. Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..
[42] David A. Landgrebe,et al. Covariance Matrix Estimation and Classification With Limited Training Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[43] Jenq-Neng Hwang,et al. Nonparametric multivariate density estimation: a comparative study , 1994, IEEE Trans. Signal Process..
[44] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[45] David A. Landgrebe,et al. Analyzing high-dimensional multispectral data , 1993, IEEE Trans. Geosci. Remote. Sens..
[46] Stephen Grossberg,et al. A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..
[47] David G. Stork,et al. Pattern Classification , 1973 .
[48] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[49] O. Ersoy,et al. Consensual and Hierarchical Classification of Remotely Sensed Multispectral Images , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.
[50] Seongwon Cho,et al. Parallel, self-organizing, hierarchical neural networks with competitive learning and safe rejection schemes , 1993 .
[51] Hugh B. Woodruff,et al. An algorithm for a selective nearest neighbor decision rule (Corresp.) , 1975, IEEE Trans. Inf. Theory.
[52] Giles M. Foody,et al. The significance of border training patterns in classification by a feedforward neural network using back propagation learning , 1999 .
[53] T. Moon. The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..
[54] John A. Richards,et al. Remote Sensing Digital Image Analysis: An Introduction , 1999 .
[55] Nello Cristianini,et al. Support vector machines , 2009 .
[56] Giles M. Foody,et al. An evaluation of some factors affecting the accuracy of classification by an artificial neural network , 1997 .
[57] William Philpot,et al. A derivative-aided hyperspectral image analysis system for land-cover classification , 2002, IEEE Trans. Geosci. Remote. Sens..
[58] G. F. Hughes,et al. On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.
[59] S. M. de Jong,et al. Imaging spectrometry : basic principles and prospective applications , 2001 .