Improving change detection methods of SAR images using fractals

Land use/cover change detection is very important in the application of remote sensing. In the case of Synthetic Aperture Radar (SAR) acquisitions for change detection, the standard detector or change measure is based on the ratio of images. However, this measure is sensitive to the speckle effect. In this paper, we improve change detection methods using a new change measure. The measure uses a grey level gradient or intensity information and the fractal dimension. The proposed measure is partitioned into two distinct regions, namely, changed and unchanged, using some change detection methods like Support Vector Machines (SVM), Fuzzy CC-Means clustering (FCM) and artificial neural networks with a back propagation training algorithm. Experiments over the study area show that the results of implementing change detection methods are improved by using the proposed measure, in comparison to the classical log-ratio image. Also, results prove that the measure is very robust to the speckle effect.