Abstract
Abstract. The goal of this paper is to extract automatically the building contours regardless of shape. By extracting these contours, detection results will be more accurate, giving useful information about urban area, which is important for many tasks, like map updating and disaster management. First, we extract local feature points from the image, based on a modification of Harris detector's saliency function, which can represent urban area and building effectively. This point set is then used to define the main orientation of the buildings, which characterizes well an urban region and helps to define directions, where object contours have to be searched. Second, we applied shearlet approach to extract edges in the defined directions. This results in an edge map, which helps us to determine point subsets belonging to the same building. Convex hulls of the point subsets is used for contour initialization, then region based Chan-Vese Active Contour method is applied to extract the accurate building outlines.
Highlights
Automatic evaluation of aerial photographs is a very important research topic, as the manual analysis is very time consuming
In our previous work (Kovacs and Sziranyi, 2012), we introduced a modification of the original Harris method, which is able to emphasize edges and corners (a) can be applied efficiently for generating a feature map for active contour approaches
The first question in applying the proposed function for the building detection task was if the proposed extended feature point set could represent the urban area. Is it possible to use these points for building detection? To get answers to these questions, we have evaluated the point set for urban area detection
Summary
Automatic evaluation of aerial photographs is a very important research topic, as the manual analysis is very time consuming. When working on optical photographs, the challenge is the large variety of features: images can be grayscale or containing poor color information, scanned in different seasons and in altering lighting conditions. In this case, pixel neighborhood processing techniques like multilayer difference image or background modeling (Benedek and Sziranyi, 2008) cannot be adopted efficiently since details are not comparable. We concentrate on building detection, which is a very important task, as land area might be changing dynamically and a continuous periodic administration is necessary to have upto-date information This is very useful for urban development analysis, map updating and helps in disaster management, vegetation monitoring and discovering illegal surface forming activities. The shape of different buildings is quite various, which needs sophisticated techniques to have more accurate results
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