Extracting Built-up Areas from Multitemporal Interferometric Sar Images

Automatic extraction of built-up areas from a multitemporal interferometric ERS-1/2 Tandem SAR dataset was studied. The image data were segmented into homogeneous regions and the regions were classified using their mean intensity and coherence values and additional contextual information. According to comparison with a set of reference points, an overall classification accuracy of 97% was achieved when classifying the dataset into three classes: built-up area, forest and open area. Reference points in densely built-up urban areas were recognized as built-up with 100% accuracy. In small-house areas the percentage of reference points correctly classified as built-up ranged from 66% to 94%, depending on the channel combination and classification rules used. Use of texture or rules related to classes of neighbouring objects improved the accuracy. The classification process was highly automatic; training areas covered only about 0.12% of the study area and did not have any overlap with the reference points used in accuracy estimation. Built-up areas could be recognized clearly better than in some previous studies with interferometric ERS data. Possibility to classify built-up areas further into subclasses was investigated using digital map data. The results suggest that the built-up classes of Finnish 1:50 000 topographic maps (small-house areas, apartment house areas and industrial areas) are difficult to distinguish reliably from each other. On the other hand, a correlation was found between the percentage of an area covered with buildings and the mean intensity and coherence of the area in the imagery. This information was used to classify built-up areas into subclasses.