Building Height Estimation Research Articles

Assessing the quality of building height extraction from ZiYuan-3 multi-view imagery

ABSTRACTThe ZiYuan-3 (ZY-3) satellite, equipped with three-line array scanners, is China’s first civilian stereo mapping satellite. In this paper, the first assessment of the stereoscopic capacities of the ZY-3 imagery for building height estimation in heterogeneous urban areas is performed. Digital surface models (DSMs) are generated and optimized by different stereo pairs of a ZY-3 triplet for the city of Wuhan, China. The normalized DSMs (nDSMs) are computed as the height of the off-terrain objects by using morphological top-hat by reconstruction of the DSMs. We adopt random forest classification and object-based building segmentation to detect the buildings from the orthographic image, and estimate the building height by assigning the maximum nDSM value within the building regions. The actual heights of 400 buildings are used as reference data. Comparisons between the building heights extracted from the different stereo pairs are presented, indicating a higher accuracy by the nadir-forward stereo pair. The performance of the ZY-3 triplet is also compared with that of a WorldView-2 (WV-2) stereo pair, better result is achieved by ZY-3 nadir-forward image pair.

Satellite images analysis for shadow detection and building height estimation

Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation.

An elevation difference model for building height extraction from stereo-image-derived DSMs

This article investigates the scale issue of inaccurate elevation around buildings in digital surface models (DSMs) and its application in building height estimation. DSMs derived from a single pair of optical stereo images are affected by occlusions and shadows, which lead to indistinct building borders in the DSM. To explore the parameters of how elevation changes in such inaccurate DSMs around buildings, a ‘building–ground elevation difference model’ (EDM) has been designed in this study. This model describes the trend of elevation differences between a building and its neighbours in order to find a stable ground elevation and to estimate actual building height. The EDM is discussed in application to both flat and sloped ground situations. Experiments on two study sites using the proposed model demonstrate that the estimated height at rooftop points can be comparable to light detection and ranging data with respect to rooftop height estimation, which outperforms the conventional filtering method. Furthermore, the proposed semi-variogram model also sheds light on the scale issue of features in DSMs of different spatial resolutions.

Performance of Building Height Estimation Using High-Resolution PolInSAR Images

This paper investigates the use of polarimetry to improve the estimation of the height of buildings in high-resolution synthetic aperture radar (SAR) images. Polarimetric coherence optimization techniques solve the problem of layover effects in urban scenes by allowing a phase separation of the scatterers sharing the same resolution cell. Bare soil elevation estimation is also improved by the polarimetric phase diversity. First, we present an analysis of the statistical modeling of the generalized coherence set. A building height estimation method is then derived from this analysis. Finally, the method is tested and quantitatively validated over an X-band polarimetric interferometric SAR (PolInSAR) airborne image acquired in a single-pass mode, containing a set of 140 different buildings with ground truth.

A stereo image matching method to improve the DSM accuracy inside building boundaries

In a digital surface model derived from high-resolution stereo images, the accuracies of building rooftop elevations are usually much lower than that of open areas without tall objects. This problem makes building height estimation difficult from the stereo images. The inaccuracy of building rooftop elevation is caused by many factors including shadows, occlusions, smoothing constraints in the matching algorithms, and the mismatch on extremely high buildings. To improve the accuracy of building rooftop elevations, this study enhances the existing image matching methods by adding building footprint maps as a constraint. The proposed image matching method consists of three steps. First, a building footprint was used to identify the corresponding building rooftop locations in the stereo images. Second, the left and right stereo images were matched according to color and shape. Third, the left and right sub-images of the building were further matched at pixel level to generate detailed roof elevations. Validation using surveyed rooftop elevations demonstrated that the proposed method can estimate building rooftop elevation with a one-third error using the current commercial software. In addition, the errors for low-rise and high-rise buildings were consistent in the proposed method.

An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery

Building seismic vulnerability assessment plays an important role in formulating pre-disaster mitigation strategies for developing countries. The occurrence of high-resolution satellite sensors has greatly motivated it by providing a promising approach to obtain building information. However, this also brings a big challenge to the accurate building extraction and its coherent integration with the assessment model. The main objective of this paper is to investigate how to extract building attributes from high-resolution remote sensing imagery using the object-based image analysis (OBIA) method, so as to accurately and conveniently assess building seismic vulnerability by the combination of in situ field data. A general framework for the assessment of building seismic vulnerability is presented, including (1) the extraction of building information using OBIA, (2) building height estimation, and (3) the support vector machine (SVM)-based building seismic vulnerability assessment. Particularly, an integrated solution is proposed that merges the strengths of multiple spatial contextual relationships and some typical image object measures, under the unified framework to improve building information extraction at different scale levels as well as for different interest objects. With the aid of 35 building samples from two powerful earthquakes in China, the cloud-free WorldView-2 images and some building structure parameters from field survey were used to quantity the grades of building seismic vulnerability in Wuhan Optics Valley, China. The results show that all 48 buildings among the study area have been well detected with an overall accuracy of 80.67 % and the mean error of heights estimated from building shadow is less than 2 m. This indicates that the integrated analysis strategy based on OBIA is suitable for extracting the building information from high-resolution remote sensing imagery. Additionally, the assessment results using SVM show that the building seismic vulnerability is statistically significantly related to structure types and building heights. Both the proposed OBIA method and its integration strategy with SVM are easily implemented and provide readily interpretable assessment results for building seismic vulnerability. This reveals that the proposed method has a great potential to assist urban planners for making local disaster mitigation planning through the prioritization of intervention measures, such as the reinforcement of walls and the dismantlement of endangered houses.

UPDATING BUILDING MAPS BASED ON OBJECT EXTRACTION AND BUILDING HEIGHT ESTIMATION

Abstract. This study presents a novel approach in building change detection for map updating in urban areas. Our approach is based on the integration of building object extraction and building height estimation utilizing existing 2D building maps and newly-acquired high resolution aerial orthoimages & DSMs. In our proposed scheme, the detection of newly-built buildings is carried out based on building object extraction from orthoimages and DSMs. Detection of demolished and reconstructed buildings is performed based on estimating the height of each building using existing building maps and newly-acquired DSMs. Finally the two results are integrated together for map updating. The experimental results showed that the proposed technique performed satisfactorily well in detecting newly-built buildings, demolished buildings and reconstructed buildings, and was very effective in reducing the problem of misdetection and false detection. This technique is already integrated into a practical system for building maps updating.

A Hybrid Approach for Building Extraction From Spaceborne Multi-Angular Optical Imagery

The advent of high resolution spaceborne images leads to the development of efficient detection of complex urban details with high precision. This urban land use study is focused on building extraction and height estimation from spaceborne optical imagery. The advantages of such methods include 3D visualization of urban areas, digital urban mapping, and GIS databases for decision makers. In particular, a hybrid approach is proposed for efficient building extraction from optical multi-angular imagery, where a template matching algorithm is formulated for automatic estimation of relative building height, and the relative height estimates are utilized in conjunction with a support vector machine (SVM)-based classifier for extraction of buildings from non-buildings. This approach is tested on ortho-rectified Level-2a multi-angular images of Rio de Janeiro from WorldView-2 sensor. Its performance is validated using a 3-fold cross validation strategy. The final results are presented as a building map and an approximate 3D model of buildings. The building detection accuracy of the proposed method is improved to 88%, compared to 83% without using multi-angular information.

Performance Evaluation for 3-D City Model Generation of Six Different DSMs From Air- and Spaceborne Sensors

Since remote sensing provides more and more sensors and techniques to accumulate data on urban regions, three-dimensional representations of these complex environments gained much interest for various applications. In order to obtain three-dimensional representations, one of the most practical ways is to generate Digital Surface Models (DSMs) using very high resolution remotely sensed images from two or more viewing directions, or by using LIDAR sensors. Due to occlusions, matching errors and interpolation techniques these DSMs do not exhibit completely steep walls, and in order to obtain real three-dimensional urban models including objects like buildings from these DSMs, advanced methods are needed. A novel approach based on building shape detection, height estimation, and rooftop reconstruction is proposed to achieve realistic three-dimensional building representations. Our automatic approach consists of three main modules as; detection of complex building shapes, understanding rooftop type, and three-dimensional building model reconstruction based on detected shape and rooftop type. Besides the development of the methodology, the goal is to investigate the applicability and accuracy which can be accomplished in this context for different stereo sensor data. We use DSMs of Munich city which are obtained from different satellite (Cartosat-1, Ikonos, WorldView-2) and airborne sensors (3K camera, HRSC, and LIDAR). The paper later focuses on a quantitative comparisons of the outputs from the different multi-view sensors for a better understanding of qualities, capabilities and possibilities for applications. Results look very promising even for the DSMs derived from satellite data.

Shadow detection and building-height estimation using IKONOS data

The spectral confusion between shadow and water (or other dark surfaces) often results in suboptimal urban classification performances, especially from high-resolution satellite imagery (e.g. IKONOS). A classification method was developed to incorporate spatial indices of image objects to improve the shadow/water detection. A number of spatial indices, such as size, shape and spatial neighbour of image objects, were characterized to differentiate water and shadow objects. This generated superior shadow/water detection performance compared to a traditional per-field Extraction and Classification of Homogeneous Objects (ECHO) classification method. The user's accuracies for shadow and water classes were increased to 88% and 92%, compared to 80% and 76% obtained from the traditional ECHO classification approach. Furthermore, an automated approach was developed for shadow-length and corresponding building-height estimation. The accuracy assessment suggested good results for very high buildings, especially for isolated high-rise buildings.

ICESat GLAS Data for Urban Environment Monitoring

Although the Geoscience Laser Altimeter System (GLAS) onboard the NASA Ice, Cloud and Land Elevation Satellite was not designed for urban applications, its 3-D measurement capability over the globe makes it a nice feature for consideration in monitoring urban heights. However, this has not been previously done. In this paper, we report some preliminary assessment of the GLAS data for building height and density estimation in a suburb of Beijing, China. Building heights can be directly calculated from a GLAS data product (GLA14). Because GLA14 limits height levels to six in each ground footprint, we developed a new method to remove this restriction by processing the raw GLAS data. The maximum heights measured in the field at selected GLAS footprints were used to validate the GLAS measurement results. By assuming a constant incident energy and surface reflectance within a GLAS footprint, the building density can be estimated from GLA14 or from our newly processed GLAS data. The building density determined from high-resolution images in Google Earth was used to validate the GLAS estimation results. The results indicate that the newly developed method can produce more accurate building height estimation within each GLAS footprint ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$R^{2} = 0.937$</tex></formula> , <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 6.4\ \hbox{m}$</tex></formula> , and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$n = 26$</tex></formula> ) than the GLA14 data product ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$R^{2} = 0.808$</tex></formula> , <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX"> $\hbox{rmse} = 11.5\ \hbox{m}$</tex></formula> , and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$n = 26$</tex></formula> ). However, satisfactory estimation results on building density cannot be obtained from the GLAS data with the methods investigated in this paper. Forest cover could be a challenge to building height and density estimation from the GLAS data. It should be addressed in future research.

Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions

SAR stereo image analysis for 3D information extraction is mostly carried out based on imagery taken under same-side or opposite-side viewing conditions. For urban scenes in practice stereo is up to now usually restricted to the first configuration, because increasing image dissimilarity connected with rising illumination direction differences leads to a lack of suitable features for matching, especially in the case of low or medium resolution data. However, due to two developments SAR stereo from arbitrary viewing conditions becomes an interesting option for urban information extraction. The first one is the availability of airborne sensor systems, which are capable of more flexible data acquisition in comparison to satellite sensors. This flexibility enables multi-aspect analysis of objects in built-up areas for various kinds of purpose, such as building recognition, road network extraction, or traffic monitoring. The second development is the significant improvement of the geometric resolution providing a high level of detail especially of roof features, which can be observed from a wide span of viewpoints. In this paper, high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their height from the different layover effects in views taken from orthogonal aspect angles. High level object matching is proposed that relies on symbolic data, representing suitable features of urban objects. Here, a knowledge-based approach is applied, which is realized by a production system that codes a set of suitable principles of perceptual grouping in its production rules. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows of point targets, rectangular structures or symmetries. The stereo analysis is then accomplished by means of productions that combine and match these 2D image objects and infer their height by 3D clustering. The approach is tested using real SAR data of an urban scene.

Building Characterization Using L-Band Polarimetric Interferometric SAR Data

This letter proposes a building characterization technique for L-band polarimetric interferometric synthetic aperture radar (SAR) data. This characterization consists of building identification and height estimation. Initially, a polarimetric interferometric segmentation is performed to isolate buildings from their surroundings. This classification identifies three basic categories: single bounce, double bounce, and volume diffusion. In order to compensate for the misclassifications among the volume and the double-bounce classes, interferometric phases given by the high-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) method are analyzed. Once buildings are localized, a phase-to-height procedure is applied to retrieve building height information. The method is validated using E-SAR, German Aerospace Center (DLR) fully polarimetric SAR data, at L-band, repeat-pass mode, over the Oberpfaffenhofen, Germany, test site, with a spatial resolution of 1.5 m in range and azimuth. More than 80% of buildings are retrieved with acceptably accurate height estimates.