An approach to cartographic object Extraction from high resolution satellite imagery

Of all tasks in photogrammetry the extraction of cartographic features is the most time consuming. Since the introduction of digital photogrammetry much attention therefore has been paid to the development of tools for a more efficient acquisition of cartographic features. Fully automatic acquisition of features like roads and buildings, however, appears to be very difficult and may even be impossible. The extraction of cartographic features from digital satellite imagery requires interpretation of this imagery. The knowledge one needs about the topographic objects and their appearances in satellite images in order to recognize these objects and extract the relevant object outlines is difficult to model and to implement in computer algorithms. This paper introduces Support vector Machine (SVM) based method of road extraction from high resolution remote sensing images. In this approach, road extraction was performed in two steps. In the first step, SVM was employed to classify the image into two categories: road and non-road. In the second step shape descriptor was used to extract roads from other spectrally similar objects. Results show the validity of the approach. © 2007 IEEE.