1 October 2009 Supervised classification of multispectral remote sensing images based on the nearest reduced convex hull approach
Author Affiliations +
Abstract
In this paper, we propose a novel learning algorithm, the nearest reduced convex hull (NRCH), to tackle the issue of limited training information in practical remote sensing. Before classification, the "underlying" prototypes in the training subspace of each class are approximated by constrained convex combinations of the "existing" ones of this class. By this means, the original training set of each class is eventually expanded to a reduced convex hull (RCH) manifold through which the representational capacity of the training set is greatly enlarged. During this process, good separations of different classes are well maintained by the reduced factor. Based on these RCHs, the nearest neighbor decision rule is then utilized to classify a query sample. Experimental results, obtained on different kinds of data (synthetic data and real multispectral images), show the potential of NRCH for remote sensing classification in comparison with some famous traditional classifiers including Maximum Likelihood Classifier (MLC), Back-propagation Neural Network Classifier (BP), and Support Vector Machine (SVM).
Jianjun Qing, Hong Huo, Tao Fang, "Supervised classification of multispectral remote sensing images based on the nearest reduced convex hull approach," Journal of Applied Remote Sensing 3(1), 033550 (1 October 2009). https://doi.org/10.1117/1.3253613 . Submission:
JOURNAL ARTICLE
18 PAGES


SHARE
Back to Top