Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas

Thiele, Antje; Cadario, Erich; Schulz, Karsten; Thönnessen, U.; Soergel, Uwe

doi:10.1109/tgrs.2007.898440

Cited by 154 publications

(80 citation statements)

References 22 publications

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“…The interference between adjacent buildings, especially in dense urban scenarios, may cause some shadows to be missing. Roofs do not appear very different from the ground, and sometimes the appearance of two building roofs can be completely different because of their different geometric structures, materials, and roughness [Simonetto et al, 2005;Thiele et al, 2007]. Bright building pixels that correspond to the layover and double bounce can be obtained using a classification method such as a threshold.…”

Section: Building Detectionmentioning

confidence: 99%

“…This work considered only simple buildings with rectangular shapes. Thiele et al [2007] reconstructed small and large buildings from multi-aspect InSAR images (with a spatial resolution of 0.3 m) from two orthogonal flight directions. For small-building detection (i.e., buildings with a maximum extent [length×width×height] of 8 m×8 m×4 m), only the frequently observed lines of bright double-bounce scattering are used.…”

Section: Introductionmentioning

confidence: 99%

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Detecting the number of buildings in a single high-resolution SAR image

Cao

Yang

2014

European Journal of Remote Sensing

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This paper proposes an effective scheme for detecting the number of buildings in a scene from a single high-resolution Synthetic Aperture Radar (SAR) image. The layover and double bounce echoes in SAR images are detected first as building elements, which are then split, merged, or discarded to make each patch correspond to one building. A model describing the statistical relationship between the number of buildings and the features of the detected building elements is constructed. Based on this model, large building patches are split into the proper number of small patches. This scheme is tested on 3-m and 6-m resolution TerraSAR-X images that cover two sites in different provinces of China, and its advantages and limitations are discussed.

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Section: Building Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting the number of buildings in a single high-resolution SAR image

Cao

Yang

2014

European Journal of Remote Sensing

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“…In particular the operational configuration of TerraSAR-X and TanDEM-X opens up new perspectives for this kind of applications. Building detection from InSAR data presented in the literature was mainly based on a combined analysis of magnitude and interferometric height data (Bolter 2001, Soergel et al 2003, and Thiele et al 2007a. The utilization of the magnitude signature focused mainly on analyzing layover and shadow areas; and the analysis of the interferometric heights was mostly restricted to mean height calculation within an estimated building footprint.…”

Section: Motivationmentioning

confidence: 99%

Smart Filtering of Interferometric Phases for Enhancing Building Reconstruction

Thiele

Dubois

Cadario

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The current generation of space borne high resolution SAR sensors provides high spatial resolution as well as interferometric data within short time frames. This makes such data attractive for 3D information extraction. Especially, the operational configuration of TerraSAR-X and TanDEM-X opens up new perspectives for this kind of applications. Despite of this, the interferometric phases still suffer from considerable noise, so that filtering is mandatory to enhance building reconstruction. In our previous work, we used conventional Multilook-filtering to smooth the phase signature. For large buildings acceptable filter results are shown, but signatures of small buildings and significant layover areas are destroyed by the use of large square windows. Such filters are especially inappropriate if building orientations are not aligned with the sensor flight direction. Hence, in this paper, we present modified InSAR phase filters to support 3D building reconstruction. The implementation focuses on two different strategies: on the one hand taking GIS information into account, in order to parameterize the filters accordingly, and on the other hand purely relying on the image data. The filters are tested on simulated interferometric phases and on real single-pass airborne InSAR data. Finally, filter properties are compared with current standard InSAR filters.

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“…Most studies published in recent years about building extraction from SAR images mainly rely on the availability of the interferometric SAR (InSAR) data [12,13], multi-aspect SAR data [14], stereoscopic data or some other ancillary data like GIS or optical images [11]. For example, Soergel et al [12] presented an InSAR approach for building detection, which is based on detection of strong scattering lines and shadowing of elevated buildings.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Soergel et al [12] presented an InSAR approach for building detection, which is based on detection of strong scattering lines and shadowing of elevated buildings. Thiele et al [14] proposed an approach for expression framework, an object-based SAR image segmentation method is adopted to provide the homogeneous image objects, and three categories of image object features are extracted, including the scattering characteristics, image object shape geometry characteristics and topology characteristics. Then, we implement the semantic rules by organizing image object features, and the individual building object expression based on ontological semantic description can be formed.…”

Section: Introductionmentioning

confidence: 99%

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Gui

Hui

et al. 2016

Remote Sensing

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Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations.

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Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas

Cited by 154 publications

References 22 publications

Detecting the number of buildings in a single high-resolution SAR image

Detecting the number of buildings in a single high-resolution SAR image

Smart Filtering of Interferometric Phases for Enhancing Building Reconstruction

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

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