Phase Analysis of Three-Dimensional Zernike Moment for Building Classification and Orientation in Digital Surface Model

Yan, Yiming; Zhang, Ye; Tian, Shu; Gao, Fengjiao

doi:10.1109/lgrs.2015.2496601

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Buildings were classified into a few types, such as single-and multiple-family buildings, single-and multiple-floor buildings, or north-and east-oriented buildings [17,19]. The proposed scheme, in contrast, was applied in a much more complex area, i.e., an "urban village", one type of typical Chinese region that has an urban pattern similar to slums in other developing countries.…”

Section: Advantages and Limitations Of The Proposed Schemementioning

confidence: 99%

A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images

Huang

Tao

et al. 2017

Remote Sensing

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Abstract:Building type information is crucial to many urban studies, including fine-resolution population estimation, urban planning, and management. Although scientists have developed many methods to extract buildings via remote sensing data, only a limited number of them focus on further classification of the extracted results. This paper presents a novel building type classification scheme based on the integration of building height information from LiDAR, textural, spectral, and geometric information from high-resolution remote sensing images, and super-object information from the integrated dataset. Building height information is firstly extracted from LiDAR point clouds using a progressive morphological filter and then combined with high-resolution images for object-oriented segmentation. Multi-resolution segmentation of the combined image is performed to collect super-object information, which provides more information for classification in the next step. Finally, the segmentation results, as well as their super-object information, are inputted into the random forest classifier to obtain building type classification results. The classification scheme proposed in this study is tested through applications in two urban village areas, a type of slum-like land use characterized by dense buildings of different types, heights, and sizes, in Guangzhou, China. Segment level classification of the study area and validation area reached accuracies of 80.02% and 76.85%, respectively, while the building-level results reached accuracies of 98.15% and 87.50%, respectively. The results indicate that the proposed building type classification scheme has great potential for application in areas with multiple building types and complex backgrounds. This study also proves that both building height information and super-object information play important roles in building type classification. More accurate results could be obtained by incorporating building height information and super-object information and using the random forest classifier.

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Section: Advantages and Limitations Of The Proposed Schemementioning

confidence: 99%

A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images

Huang

Tao

et al. 2017

Remote Sensing

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“…In recent years, a number of building detection methodologies were developed utilising LiDAR data, which uses remote sensing technologies to automatically estimate elevation of objects on the earth surface (Li and Guan, 2011;Sampath and Shan 2010;Siddiqui et al, 2013;Wu et al, 2017a). Huang et al (2017) separate the aim of such works into two main categories, building characteristics extraction (Chen et al, 2005;Rottensteiner, 2003;Rottensteiner and Briese, 2002) and building classification (Abella´n and Moral, 2003;Tobergte and Curtis, 2013;Yan et al, 2016). However, only a limited number of studies were carried out to estimate the floor count in specific buildings.…”

Section: Introductionmentioning

confidence: 99%

“…Huang et al. (2017) separate the aim of such works into two main categories, building characteristics extraction (Chen et al, 2005; Rottensteiner, 2003; Rottensteiner and Briese, 2002) and building classification (Abellán and Moral, 2003; Tobergte and Curtis, 2013; Yan et al., 2016). However, only a limited number of studies were carried out to estimate the floor count in specific buildings.…”

Section: Introductionmentioning

confidence: 99%

City-wide building height determination using light detection and ranging data

Blunden

Bahaj

2018

Environment and Planning B: Urban Analytics and City Science

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The research presented in this paper addresses a current gap in the availability of building geometry data and provides estimates of individual building characteristics at city scale. Such data are crucial for a wide range of subjects such as modelling building energy consumption as well as regional housing market studies. However, such data are currently not available in the UK. In this work, a new approach was developed to automatically estimate the geometric characteristics of buildings, including height and floor count. A wide range of datasets have been brought together including high-resolution light detection and ranging data to accurately estimate building elevation and to obtain the external dimension of buildings. In the UK, most of the datasets required for this model are available for urban areas, allowing the model to be widely applied both in cities and beyond. The paper presents the results of building height and floor count determined from this model and compares these with the actual data obtained from a survey of 108 representative buildings in the city of Southampton. The results show good accuracy of the model with 97% of the estimates having an error under ±1 floor and an absolute mean error of 0.3 floors. These results provide confidence in utilising this model for future building studies at a city scale.

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Building Reconstrucion Using Three-Dimensional Zernike Moments in Digital Surface Model

Zhang

Tian

2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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Phase Analysis of Three-Dimensional Zernike Moment for Building Classification and Orientation in Digital Surface Model

Cited by 5 publications

References 16 publications

A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images

A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images

City-wide building height determination using light detection and ranging data

Building Reconstrucion Using Three-Dimensional Zernike Moments in Digital Surface Model

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