Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Barragán, W.; Campos, A.; Sanchez, G.

doi:10.5194/isprsarchives-xli-b7-171-2016

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

2016

DOI: 10.5194/isprsarchives-xli-b7-171-2016

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Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

W. Barragán¹,

A. Campos²,

G. Sanchez³

Abstract: Commission VII, WG VII/4KEY WORDS: Cartography, Automatic Extraction, Photogrammetry, Aerial Photographs. Image Segmentation ABSTRACT:The objective of this research is automatic generation of buildings in the interest areas. This research was developed by using high resolution vertical aerial photographs and the LIDAR point cloud through radiometric and geometric digital processes. The research methodology usesknown building heights and various segmentation algorithms and spectral band combination. The overall… Show more

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2019

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A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

Qin

Zhang

et al. 2019

Remote Sensing

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Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

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A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

Qin

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

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Automatic Generation of Building Mapping Using Digital, Vertical and Aerial High Resolution Photographs and Lidar Point Clouds

Cited by 1 publication

References 5 publications

A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

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