Improving change detection methods of SAR images using fractals

Land use/cover change detection is very important in the application of remote sensing. in thecase of synthetic aperture radar (sar) acquisitions for change detection, the standard detector or changemeasure is based on the ratio of images. however, this measure is sensitive to the speckle effect. in thispaper, we improve change detection methods using a new change measure. the measure uses a grey levelgradient or intensity information and the fractal dimension. the proposed measure is partitioned into twodistinct regions, namely, changed and unchanged, using some change detection methods like supportvector machines (svm), fuzzy c-means clustering (fcm) and artificial neural networks with a backpropagation training algorithm. experiments over the study area show that the results of implementingchange detection methods are improved by using the proposed measure, in comparison to the classicallog-ratio image. also, results prove that the measure is very robust to the speckle effect.