Unsupervised change detection in satellite images using fuzzy c-means clustering and principal component analysis

• Main Authors: M. H. Kesikoğlu, Ü. H. Atasever, C. Özkan
• Format: Article
• Language: English
• Published: Copernicus Publications 2013-10-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W2/129/2013/isprsarchives-XL-7-W2-129-2013.pdf

Change detection analyze means that according to observations made in different times, the process of defining the change detection occurring in nature or in the state of any objects or the ability of defining the quantity of temporal effects by using multitemporal data sets. There are lots of change detection techniques met in literature. It is possible to group these techniques under two main topics as supervised and unsupervised change detection. In this study, the aim is to define the land cover changes occurring in specific area of Kayseri with unsupervised change detection techniques by using Landsat satellite images belonging to different years which are obtained by the technique of remote sensing. While that process is being made, image differencing method is going to be applied to the images by following the procedure of image enhancement. After that, the method of Principal Component Analysis is going to be applied to the difference image obtained. To determine the areas that have and don’t have changes, the image is grouped as two parts by Fuzzy C-Means Clustering method. For achieving these processes, firstly the process of image to image registration is completed. As a result of this, the images are being referred to each other. After that, gray scale difference image obtained is partitioned into 3 × 3 nonoverlapping blocks. With the method of principal component analysis, eigenvector space is gained and from here, principal components are reached. Finally, feature vector space consisting principal component is partitioned into two clusters using Fuzzy C-Means Clustering and after that change detection process has been done.