Unsupervised classification approaches, also known as “clustering algorithms”, can be
considered a solution to problems associated with the supervised classification of remotely …

The rapid development of earth observation technology has produced large quantities of
remote-sensing data. Unsupervised classification (ie clustering) of remote-sensing images …

Image segmentation can be cast as a clustering task where the image is partitioned into
clusters. Pixels within the same cluster are as homogenous as possible whereas pixels …

Clustering algorithms are affected by the initial seeds, therefore any improvement of the
initialization process can improve the final clustering results. There exist several initialization …

Hyperspectral data classification using supervised approaches, in general, and the
statistical algorithms, in particular, need high quantity and quality training data. However …

As compared to general RGB images which contain only three bands from visible range of
electromagnetic spectra, hyperspectral images contain several spectral bands covering a …

Fuzzy based segmentation algorithms are known to be performing well on medical images.
Spatial fuzzy C-means (SFCM) is broadly used for medical image segmentation but it suffers …

Segmentation is the process of dividing the original image into multiple sub regions called
segments in such a way that there is no intersection between any two regions. In medical …

One of the most challenging problems in automated clustering of hyperspectral data is
determining the number of clusters (NOC) either prior to or during the clustering. We …

In recent decades, large margin methods such as Support Vector Machines (SVMs) are
supposed to be the state-of-the-art of supervised learning methods for classification of …