Semi-automatic extraction of large and moderate buildings from very high-resolution satellite imagery using active contour model

The traditional pixel-based classification totally relies on spectral information and neglects the spatial information. These methods when applied on very high-resolution imagery get confused because of the increased variability implicit within the data and thus leads to lower classification accuracies. The object-based image analysis (OBIA) is advantageous to deal with objects that are composed of homogeneous pixels. This paper aims at automatically extracting buildings from very high-resolution satellite imagery using Object Based Image Analysis(OBIA). The algorithm uses an active contour model called chan-vese segmentation to create objects from the image. Objects representing vegetation or trees are removed by subtracting NDVI mask from the segmented output. The detected objects are further filtered based on regional properties like minimum area, width of object etc. The results are promising with 74–77% of the buildings getting detected as objects.