DocumentCode :
1453968
Title :
Memory-Based Cluster Sampling for Remote Sensing Image Classification
Author :
Volpi, Michele ; Tuia, Devis ; Kanevski, Mikhail
Author_Institution :
Inst. of Geomatics & Anal. of Risk, Univ. of Lausanne, Lausanne, Switzerland
Volume :
50
Issue :
8
fYear :
2012
Firstpage :
3096
Lastpage :
3106
Abstract :
In this paper, we address the problem of semi-automatic definition of training sets for the classification of remotely sensed images. We propose two approaches based on active learning aiming at removing both the proximal (low diversity) and the dense (low exploration during iterations) sampling redundancies. The first is encountered when several samples carrying similar spectral information are selected by the algorithm, while the second occurs when the heuristic is unable to explore undiscovered parts of the feature space during iterations. For this purpose, kernel k-means is used to cluster a set of uncertain candidates in the same space spanned by the kernel function defined in the SVM classification step. Two heuristics are proposed to maximize the speed of convergence to high classification accuracies: The first is based on binary hierarchical partitioning of the set of selected uncertain samples, while the second extends this approach by considering memory in the selection and thus dynamically adapts to the problem throughout the iterations. Experiments on both VHR and hyperspectral imagery confirm fast convergence of the algorithm, that outperforms state-of-the-art sampling schemes.
Keywords :
geophysical image processing; image classification; remote sensing; SVM classification step; dense sampling redundancies; image classification; kernel k-means; low diversity; low exploration; memory based cluster sampling; proximal sampling redundancies; remote sensing; semiautomatic definition; Clustering algorithms; Convergence; Kernel; Redundancy; Support vector machines; Training; Uncertainty; Active learning; batch sampling; hyperspectral imagery; informative sampling; kernel-based clustering; support vector machines (SVM); very high resolution (VHR);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2011.2179661
Filename :
6155743
Link To Document :
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