This work presents a method for detection, localization, classification and pose estimation of objects in SAR-image sequences. Such methods have to deal with strong noise in SAR-images and have the challenge that shadows, which may occur, should not affect the recognition process. The disturbing effect of noise is significantly reduced in the presented method by temporal integration of the SAR-images, using a motion-model of the sensor. Thus it is possible to perform a segmentation on the integrated images with quantile-thresholds and a region growing algorithm using an edge image created by a Canny-edge detector. To be independent of the number of objects in the image and the brightness of the image, a multi-threshold approach is used. By accumulating the segmented images, following an analysis of the homogeneity of the accumulated segments, it is possible to identify stable segments as possible objects. An optimization process is used to fit a generic model of a house into the stable segments. As initial values for the optimization process the results of a connected-pixel algorithm are used. An application example is presented, in which house-objects can be separated from shadows in a village formation and their pose can be determined correctly.