Feature extraction using very high resolution satellite imagery

Abstract

With the availability of very high resolution commercial satellite data, there has been much interest to extract man-made objects from such imagery. In this paper we propose a PC-based incremental system which includes both road and building extraction tasks in a same package, and makes use of IKONOS's stereo pair to generate 3-D city model. For each image set, we first use the traditional Normalized Difference Vegetation Index (NDVT) to locate the vegetated areas to be used for masking, and apply the Canny operator to the panchromatic images for edge detection. Subsequently we use the edge thinning and division algorithm to enhance the detected edges. The roads in satellite imagery are extracted in a semi-automatic way. After the major road areas have been extracted, we focus the search for buildings in areas that are neither road nor vegetation instead of searching the whole image. The package also provide user interactions which make use of some existing techniques for rooftop hypotheses generation. To handle buildings of different sizes in an image, we use a multi-level approach to make 3D building model generation more efficient.