A comprehensive study on urban true orthorectification

Zhou, Guoqing; Chen, Weirong; Kelmelis, John A.; Zhang, Deyan

doi:10.1109/tgrs.2005.848417

Cited by 91 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The orthorectification procedures, unlike most image-toimage registration methods, bypass the steps of finding common features and estimating transformation model parameters, required in conventional registration methods, by utilizing the image acquisition information. A comprehensive study on the urban true orthorectification process is provided by [17]. In this process, elevation data are used and the sensor model information of the employed perspective images are incorporated to eliminate the effects of the offnadir perspective image acquisition and hence perfectly align image to elevation data [18].…”

Section: Previous Workmentioning

confidence: 99%

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Suliman¹,

Zhang²

2018

JGIS

View full text Add to dashboard Cite

The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

show abstract

Section: Previous Workmentioning

confidence: 99%

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Suliman¹,

Zhang²

2018

JGIS

View full text Add to dashboard Cite

show abstract

“…However, the specific characteristics of urban areas make the collection of this information complex. Indeed, the city is characterized by strong internal dynamics, a very large spatial heterogeneity of its elements, its geometric shapes (horizontal and vertical) (Small et al, 2003), its great variety of materials (Small, 2005) and the presence of shadows due to its three-dimensional geometry (Zhou et al, 2005). Understanding the functioning of the urban system requires the identification and characterization of specific observables such as buildings and their morphology, mineral surfaces, wastelands, aquatic surfaces or vegetation at different levels.…”

Section: Introductionmentioning

confidence: 99%

Imagerie Hyperspectrale Et Zones Urbaines: Résultats Du Projet HYEP

Weber

Briottet²,

HOUET³

et al. 2022

RFPT

View full text Add to dashboard Cite

Le projet HYEP (ANR HYEP 14-CE22-0016-01 HYperspectral imagery for Environmental urban Planning - Programme Mobilité et systèmes urbains - 2014) a permis de confirmer l’intérêt d’une approche globale du milieu urbain par télédétection et notamment en utilisant l’imagerie hyperspectrale (IH). L’intérêt des images hyperspectrales réside dans la gamme d’informations fournies sur des longueurs d’onde de 0,4 à 4 μm ; elles fournissent ainsi un accès aux grandeurs spectrales d'intérêt et aux paramètres chimiques ou biophysiques de la surface. HYEP avait pour objectif de préciser ceci et de proposer un panel de méthodes et de traitements en tenant compte des caractéristiques des autres capteurs existants afin d’en comparer les performances. Les développements réalisés ont été appliqués et évalués sur des images aéroportées hyperspectrales acquises sur Toulouse (France) et Kaunas (Lituania), utilisées également pour synthétiser des systèmes spatiaux : Sentinel-2, Hypxim/Biodiversity et Pleiades. Parmi les verrous identifiés, ceux liés à l'amélioration des capacités spatiales des capteurs et aux changements d’échelle spatiale ont été en partie levés par des approches de fusion, qui se sont avérées concluantes. Des méthodes d’extraction, de fusion ou de classification ont été appliquées à différentes résolutions spatiales afin de qualifier les gains et la complémentarité de l’IH par rapport à d’autres capteurs. Ces évaluations ont ainsi porté sur des « objets » d’intérêt pour les gestionnaires et aménageurs du milieu urbain : les surfaces imperméables, la végétation ou encore la détection de panneaux solaires.

show abstract

“…This method greatly improves the tracking efficiency. Zhou et al [15] detected the building shadows by using a model which is stored in a DBM database. They found that the length of the shadow is determined by height of the building and the solar zenith angle, the direction of the shadow is determined by the position of the sun relative to the building, and the solar zenith angle at the time of imaging remains constant.…”

Section: Introductionmentioning

confidence: 99%

“…Orthoimage is basically an orthorectified or geometrically corrected aerial image having a uniform scale and can be used as a map. However, ghost image refers to the phenomenon where the same building roof appears twice in the orthoimage because the roof of the building is superimposed on the bottom of the building [15], and the ghost image does not consider the effect of shadows. Therefore, our method is effective in detecting the complete shadows of even complex buildings, and the shadow areas located on the building roofs.…”

Section: Introductionmentioning

confidence: 99%

Building Shadow Detection on Ghost Images

Zhou

Sha

2020

Remote Sensing

View full text Add to dashboard Cite

Although many efforts have been made on building shadow detection from aerial images, little research on simultaneous shadows detection on both building roofs and grounds has been presented. Hence, this paper proposes a new method for simultaneous shadow detection on ghost image. In the proposed method, a corner point on shadow boundary is selected and its 3D approximate coordinate is calculated through photogrammetric collinear equation on the basis of assumption of average elevation within the aerial image. The 3D coordinates of the shadow corner point on shadow boundary is used to calculate the solar zenith angle and the solar altitude angle. The shadow areas on the ground, at the moment of aerial photograph shooting are determined by the solar zenith angle and the solar altitude angle with the prior information of the digital building model (DBM). Using the relationship between the shadows of each building and the height difference of buildings, whether there exists a shadow on the building roof is determined, and the shadow area on the building roof on the ghost image is detected on the basis of the DBM. High-resolution aerial images located in the City of Denver, Colorado, USA are used to verify the proposed method. The experimental results demonstrate that the shadows of the 120 buildings in the study area are completely detected, and the success rate is 15% higher than the traditional shadow detection method based on shadow features. Especially, when the shadows occur on the ground and on the buildings roofs, the successful rate of shadow detection can be improved by 9.42% and 33.33% respectively.

show abstract

A comprehensive study on urban true orthorectification

Cited by 91 publications

References 19 publications

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Imagerie Hyperspectrale Et Zones Urbaines: Résultats Du Projet HYEP

Building Shadow Detection on Ghost Images

Contact Info

Product

Resources

About