Change detection in remotely sensed images using semi-supervised clustering algorithms
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.
Authors: Moumita Roy, Susmita Ghosh, Ashish Kumar Ghosh
Published in: International Journal of Knowledge Engineering and Soft Data Paradigms (2013)