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15 October 2015 Unsupervised and stable LBG algorithm for data classification: application to aerial multicomponent images
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Abstract
In this paper a stable and unsupervised Linde-Buzo-Gray (LBG) algorithm named LBGO is presented. The originality of the proposed algorithm relies: i) on the utilization of an adaptive incremental technique to initialize the class centres that calls into question the intermediate initializations; this technique makes the algorithm stable and deterministic, and the classification results do not vary from a run to another, and ii) on the unsupervised evaluation criteria of the intermediate classification result to estimate the optimal number of classes; this makes the algorithm unsupervised.

The efficiency of this optimized version of LBG is shown through some experimental results on synthetic and real aerial hyperspectral data. More precisely we have tested our proposed classification approach regarding three aspects: firstly for its stability, secondly for its correct classification rate, and thirdly for the correct estimation of number of classes.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Taher, K. Chehdi, and C. Cariou "Unsupervised and stable LBG algorithm for data classification: application to aerial multicomponent images", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96431I (15 October 2015); https://doi.org/10.1117/12.2191448
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