Int. J. of Knowledge Engineering and Soft Data Paradigms   »   2013 Vol.4, No.2

 

 

Title: Change detection in remotely sensed images using semi-supervised clustering algorithms

 

Authors: Moumita Roy; Susmita Ghosh; Ashish Ghosh

 

Addresses:
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
Center for Soft Computing Research, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India

 

Abstract: Scarcity of sufficient ground truth information is the primary bottleneck for adopting any supervised methodology in change detection domain and hence, unsupervised approaches are mostly used for this task. But, with a few labelled patterns in hand, semi-supervised methods can be chosen instead of unsupervised ones to utilise both the labelled and unlabelled patterns completely. Work on semi-supervised learning (both in the areas of clustering and classification) is now being explored. In this article, a detailed study has been made by applying some of the semi-supervised clustering techniques for change detection. In present investigation, five semi-supervised clustering techniques, namely COP-KMeans, seeded-KMeans, constrained-KMeans, semi-supervised-HMRF-KMeans and semi-supervised-kernel-KMeans algorithms are used. A comparative analysis has been made among these algorithms and standard K-Means algorithm, using two multi-temporal remotely sensed images and are also statistically validated using paired t-test. Experimental results conclude that constrained-KMeans for both the datasets is more applicable for change detection than COP-KMeans and seeded-KMeans. Semi-supervised-HMRF-KMeans and semi-supervised-kernel-KMeans algorithms are found not to be robust for all the datasets because these algorithms outperform constrained-KMeans in case of only one dataset.

 

Keywords: multi-temporal images; semi-supervised clustering; change detection; remote sensing; clustering algorithms; k-means clustering.

 

DOI: 10.1504/IJKESDP.2013.058127

 

Int. J. of Knowledge Engineering and Soft Data Paradigms, 2013 Vol.4, No.2, pp.118 - 137

 

Available online: 08 Dec 2013

 

 

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