Classification in High-Dimensional Feature Spaces—Assessment Using SVM, IVM and RVM With Focus on Simulated EnMAP Data

Support Vector Machines (SVM) are increasingly used in methodological as well as application oriented research throughout the remote sensing community. Their classification accuracy and the fact that they can be applied on virtually any kind of remote sensing data set are their key advantages. Especially researchers working with hyperspectral or other high dimensional datasets tend to favor SVMs as they suffer far less from the Hughes phenomenon than classifiers designed for multispectral datasets do. Due to these issues, numerous researchers have published a broad range of enhancements on SVM. Many of these enhancements aim at introducing probability distributions and the Bayes theorem. Within this paper, we present an assessment and comparison of classification results of the SVM and two enhancements-Import Vector Machines (IVM) and Relevance Vector Machines (RVM)-on simulated datasets of the Environmental Mapping and Analysis Program EnMAP.

[1]  Uwe Weidner,et al.  Support vector machines, import vector machines and relevance vector machines for hyperspectral classification — A comparison , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[2]  Jocelyn Chanussot,et al.  Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Antonio J. Plaza,et al.  The Future of Imaging Spectroscopy Prospective Technologies and Applications , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[4]  Ji Zhu,et al.  Kernel Logistic Regression and the Import Vector Machine , 2001, NIPS.

[5]  Anthony M. Filippi,et al.  Support Vector Machine-Based Endmember Extraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Francesca Bovolo,et al.  Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Gustavo Camps-Valls,et al.  Retrieval of oceanic chlorophyll concentration with relevance vector machines , 2006 .

[8]  Luis Guanter,et al.  Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[9]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[10]  Begüm Demir,et al.  Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Michael E. Tipping,et al.  Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .

[12]  Melba M. Crawford,et al.  Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Ribana Roscher,et al.  Kernel Discriminative Random Fields for land cover classification , 2010, 2010 IAPR Workshop on Pattern Recognition in Remote Sensing.

[14]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[15]  H. Kaufmann,et al.  Hyperspectral imaging—An advanced instrument concept for the EnMAP mission (Environmental Mapping and Analysis Programme) , 2009 .

[16]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .