Translator Disclaimer
Paper
24 October 2007 Investigation of an ensemble framework for classification of hyperspectral remote sensing data with nearly equal spectral response classes
Author Affiliations +
Abstract
This paper investigates an ensemble framework which is proposed for accurate classification of hyperspectral data. The usefulness of the method, designed to be a simple and robust supervised classification tool, is assessed on real data, characterized by classes with very similar spectral responses, and limited amount of ground truth labeled training samples. The method is inspired by the framework of the Random Forests method proposed by Breiman (2001). The success of the method relies on the use of support vector machines (SVMs) as base classifiers, the freedom of random selection of input features to create diversity in the ensemble, and the use of the weighted majority voting scheme to combine classification results. Although not fully optimized, a simple and feasible solution is adopted for tuning the SVM parameters of the base classifiers, aiming its use in practical applications. Moreover, the effect of an additional pre-processing module for an initial feature reduction is investigated. Encouraging results suggest the proposed method as promising, in addition to being easy to implement.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maciel Zortea, Gabriele Moser, and Sebastiano B. Serpico "Investigation of an ensemble framework for classification of hyperspectral remote sensing data with nearly equal spectral response classes", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480P (24 October 2007); https://doi.org/10.1117/12.738308
PROCEEDINGS
12 PAGES


SHARE
Advertisement
Advertisement
Back to Top