Computer processing of remotely-sensed images: an introduction
Computer processing of remotely-sensed images: an introduction
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
A survey of multiple classifier systems as hybrid systems
Information Fusion
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The accuracy attained in the mapping of underwater areas is limited by the effect of variations in the water column, which degrade the signal received by the orbital sensor, creating interclasses confusion that introduce errors into the final result of the classification process. In this article we will describe a hybrid classifier ensembles; the classification is done by progressive refining in three stages. At the end of this process, a combining unit links the various partial classifications generated and achieve the accuracy level desired. At the end, the result obtained by the ensemble is compared to the results achieved by the application of multi-class voting scheme methods based on support vector machine: One-Against-the-Rest and One-Against-One. Classification accuracy showed the viability and the potential of using the proposed ensemble to classify images.