An efficient use of random forest technique for SAR data classification

In the past SAR data has been proven as a great source for land cover characterization. For classification purpose many individual methods has been used, but single method are likely to undergo high variance or biasness depending on the base used for classification. Hence, in this paper random forest classification technique has been used for SAR data classification into different land cover classes (urban, water, vegetation and bare soil) which minimizes the diversity amongst the fragile classifiers and produce more accurate predictions. In this regard, an attempt has been made to fuse, four types of measures, namely texture features, SAR observable, statistical features and color features using random forest classifier for land cover classification. The results show that the resultant classified image has better accuracy in comparison to the individual method.