Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings

Mayer, Helmut

doi:10.1006/cviu.1999.0750

Cited by 291 publications

(151 citation statements)

References 60 publications

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“…Examples are detection of buildings in remotely sensed images [24,19,14,3,12,15,1,31,30,23,26], traffic signs [21,13,22], and particles of a rectangular shape in cryo-electron microscopy images [36,35]. The methods used were based on Markov Random Fields [15,21], Marked Point Processes [1,26], search on a graph [14,38], Hough Transform and other voting schemes [3,12,36,13,22], template matching [25], aggregation of local features [1,23,31], and heuristic rules [19].…”

Section: Related Workmentioning

confidence: 99%

Detection of incomplete enclosures of rectangular shape in remotely sensed images

Zingman

Saupe

Lambers

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Section: Related Workmentioning

confidence: 99%

Detection of incomplete enclosures of rectangular shape in remotely sensed images

Zingman

Saupe

Lambers

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…Building detection in single monocular aerial images is very hard without additional elevation data [9]. Mayer's survey [10] describes some existing systems for building detection and concludes that scale, context and 3D structure were the three most important features to consider for object extraction in aerial images. Fusion of SAR (Synthetic Aperture Radar) and aerial images has been employed for detection of building outlines [9].…”

Section: Related Workmentioning

confidence: 99%

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Persson

Duckett

Lilienthal

2008

Robotics and Autonomous Systems

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This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

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“…Techniques that are specifically designed for detection of buildings can be found in the literature [5]. Our goal in this work is to evaluate measures of arrangements of buildings so we developed a simple detector for individual buildings.…”

Section: Building Detectionmentioning

confidence: 99%

Modeling urban structures using graph-based spatial patterns

Dogrusoz

Aksoy

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-We introduce a new method for modeling the spatial arrangements of geospatial objects. As opposed to the existing approaches that are based on classifying images using pixel level methods, we propose to use objects as textural primitives and exploit their spatial patterns. First, the primitives are detected using spectral and morphological processing. Then, these primitives form the nodes of a graph where the neighborhood information is obtained through Voronoi tessellation of the image scene. Next, this graph is clustered by thresholding its minimum spanning tree. Finally, the resulting clusters are classified as regular or irregular by examining the distributions of the angles between neighboring nodes. Experiments using Ikonos images show that the application of the proposed model where buildings are used as the primitives and building groups are automatically classified as organized or unorganized can extract valuable information about urban development.

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Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings

Cited by 291 publications

References 60 publications

Detection of incomplete enclosures of rectangular shape in remotely sensed images

Detection of incomplete enclosures of rectangular shape in remotely sensed images

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Modeling urban structures using graph-based spatial patterns

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