A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Random Forests for land cover classification
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
FaSS: Ensembles for Stable Learners
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Influence of Hyperparameters on Random Forest Accuracy
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Disturbing Neighbors Ensembles for Linear SVM
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Study of Semi-supervised Generative Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Multiple classifier system for urban area's extraction from high resolution remote sensing imagery
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
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In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied to multisource data (satellite multispectral images, elevation, slope and aspect data) for landcover classification. In the second part, special attention is given to Support Vector Machines (SVM) based algorithms. In particular, the fusion of two classifiers using both spectral and the spatial information is discussed in the frame of hyperspectral remote sensing for the classification of urban areas. In all the cases, MCS provide a significant improvement of the classification accuracies. In order to address new challenges for the analysis of remote sensing data, MCS provide invaluable tools to handle situations with an ever growing complexity. Examples include extraction of multiple features from one data set, use of multi-sensor data, and complementary use of several algorithms in a decision fusion scheme.