Land cover change detection using unsupervised kernel C-means and multi-temporal SAR data

Land covers and uses are dynamically being changed over the time. Detection and identification of these changes is necessary and is the first step of any study or planning for natural resource management. Synthetic Aperture Radar (SAR) imagery, thanks to its independence to weather conditions and sun illumination, is a powerful tool for these studies. In this research an unsupervised change detection framework based on the kernel-based clustering technique is presented. Kernel C-means algorithm is employed to separate the changes classes from the no-changes. This method is a non-linear algorithm which considers the contextual information. Using the kernel functions, the projecting of the data into a higher dimensional space helps to make the non-linear features more separable in a linear space. The proposed methodology has applied to dual-pol L-band SAR images acquired by the ALOS from Urmia Lake. Results show because of non-linear behavior of changed phenomenon, the algorithm leads to more reliable results.