Class-guided building extraction from Ikonos imagery

Recent high-resolution satellite images provide a valuable new data source for geospatial information acquisition. This paper addresses building extraction from Ikonos images in urban areas. The proposed approach uses the classification results of Ikonos multispectral images to provide approximate location and shape for candidate building objects. Their fine extraction is then carried out in the corresponding panchromatic image through segmentation and squaring. The ECHO classifier is used for supervised classification while the ISODATA algorithm is used for unsupervised classification and subsequent image segmentation. The classification performance is evaluated using the classification confusion matrix, while the final building extraction results are assessed based on the manually delineated results. A building squaring approach based on the Hough transformation is developed that detects and forms the rectilinear building boundaries. A number of sample results are presented to illustrate the approach and demonstrate its efficiency. It is shown that about 64.4 percent of the buildings can be detected, extracted, and accurately formed through this process. Remaining difficulties are high percentage false alarm errors caused by the misclassification of road and building classes as well as occlusion and shadows that may mislead the extraction. process.