A stable and unsupervised version of the fuzzy C-means algorithm, named FCM-optimized (FCMO), is presented. The originality of the proposed algorithm stems from (1) the introduction of an adaptive incremental procedure to initialize class centers, which makes the algorithm stable and deterministic; therefore, the classification results do not vary from one run to another and (2) the use of an unsupervised evaluation criterion to estimate the optimal number of classes. The validation of FCMO with regard to stability, reliability in class number estimation, and classification efficiency is shown through experimental results on synthetic monocomponent and real multicomponent images.
In this paper a stable and unsupervised Linde-Buzo-Gray (LBG) algorithm named LBGO is presented. The originality of the proposed algorithm relies: <i>i)</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 <i>ii)</i> on the unsupervised evaluation criteria of the intermediate classification result to estimate the optimal number of classes; this makes the algorithm unsupervised. <p> </p>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.
In this paper a stable and unsupervised version of FCM algorithm named FCMO is presented. The originality of the proposed FCMO algorithm relies: i) on the usage of an adaptive incremental technique to initialize the class centres that calls into question the intermediate initializations; this technique renders 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 FCM is shown through some experimental results for its stability and its correct class number estimation.
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is
presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are
evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are
used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to
segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of
regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and
validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management
of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment
steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the
spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature
extraction step, up to the final classification results. This approach was first evaluated on a large number of
monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a
respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image
for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate
(CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR)
is over 99%.