Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building detection and description from a single intensity image
Computer Vision and Image Understanding
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
Uncertain reasoning and learning for feature grouping
Computer Vision and Image Understanding
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Assessment of urban-scale wireless networks with a small number of measurements
Proceedings of the 14th ACM international conference on Mobile computing and networking
EURASIP Journal on Advances in Signal Processing
A novel approach for polygonal rooftop detection in satellite/aerial imageries
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Robust model-based detection of gable roofs in very-high-resolution aerial images
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
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High-resolution satellite imagery provides an important new data source for building extraction. We demonstrate an integrated strategy for identifying buildings in 1-meter resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spectral information. First, a series of geodesic opening and closing operations are used to build a differential morphological profile (DMP) that provides image structural information. Building hypotheses are generated and verified through shape analysis applied to the DMP. Second, shadows are extracted using the DMP to provide reliable contextual information to hypothesize position and size of adjacent buildings. Seed building rectangles are verified and grown on a finely segmented image. Next, bright buildings are extracted using spectral information. The extraction results from the different information sources are combined after independent extraction. Performance evaluation of the building extraction on an urban test site using IKONOS satellite imagery of the City of Columbia, Missouri, is reported. With the combination of structural, contextual, and spectral information, 72.7% of the building areas are extracted with a quality percentage 58.8%.