Performance Evaluation of Object-based and Pixel-based Building Detection Algorithms from Very High Spatial Resolution Imagery
This paper reviews and evaluates four building extraction algorithms including two pixel-based and two object-based methods using a diverse set of very high spatial resolution imagery. The applied images are chosen from different places (the cities of Isfahan, Tehran, and Ankara) and different sensors (QuickBird and GeoEye-1), which are diverse in terms of building shape, size, color, height, alignment, brightness, and density. The results indicate that the performance and the reliability of two object-based algorithms are better than pixel-based algorithms; about 10 percent to 15 percent better for the building detection rate and 6 percent to 10 percent better for the reliability rate. However, in some cases, the detection rate of pixel-based algorithms has been greater than 80 percent, which is a satisfactory result. On the other hand, segmentation errors can cause limitations and errors in the object-based algorithms, so that the commission error of object-based algorithms has been higher than pixel-based algorithms in some cases.
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Document Type: Research Article
Publication date: June 1, 2014
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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