When disasters strike in urban areas, the most important issue is to direct rescue forces to the most heavily destroyed areas. SAR images, because of their independence from daylight and weather conditions, are the remote sensing tool of choice in these cases. However, often no pre-event image is available, so change detection cannot be performed. Thus, we aim at extracting areas of debris from a single post-event SAR image using textural features. We want to be independent of real samples of debris sites by using simulated SAR image chips. Previous work has shown that in this way we detect all major sites of debris, e.g. caused by collapsed buildings. However, the screening process also detects many other areas, especially high vegetation and gravel. In order to rule these areas out from the analysis, it is important to also simulate these classes of objects. The simulated chips can then be used in a classifier, specifically a random forest, to rule out these causes of false alarms.