Skip to Main content Skip to Navigation
Conference papers

Unsupervised and stable LBG algorithm for data classification: application to aerial multicomponent images

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
Complete list of metadatas

https://hal-univ-rennes1.archives-ouvertes.fr/hal-01300963
Contributor : Laurent Jonchère <>
Submitted on : Monday, April 11, 2016 - 3:46:39 PM
Last modification on : Monday, October 5, 2020 - 9:50:27 AM

Identifiers

Citation

A. Taher, K. Chehdi, Claude Cariou. Unsupervised and stable LBG algorithm for data classification: application to aerial multicomponent images. Image and Signal Processing for Remote Sensing XXI, 96431I (October 15, 2015), 2015, Toulouse, France. pp.96431I--96431I--9, ⟨10.1117/12.2191448⟩. ⟨hal-01300963⟩

Share

Metrics

Record views

172