A supervised method for object-based 3D building change detection on aerial stereo images

Ran, Qin; Gruen, Armin

doi:10.5194/isprsarchives-xl-3-259-2014

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Huang et al [27] proposed an object-based time-series newly constructed building areas detection method using both planar and vertical features. Supervised classification was also introduce into the 3D change detection algorithm, including SVM [28], decision-tree [23], and random forest [29]. However, the training label was generated by a rule set method, which has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery

Wang

Zhang

et al. 2022

Remote Sensing

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Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D change detection algorithms are based on the independent segmentation of two-temporal images and the feature fusion of spectral change and height change. These methods do not consider 3D change information and spatial context information simultaneously. In this paper, we propose a novel building change detection algorithm based on 3D Co-segmentation, which makes full use of the 3D change information contained in the stereoscope data. An energy function containing spectral change information, height change information, and spatial context information is constructed. Image change feature is extracted using morphological building index (MBI), and height change feature is obtained by robust normalized digital surface models (nDSM) difference. 3D Co-segmentation divides the two-temporal images into the changed foreground and unchanged background through the graph-cut-based energy minimization method. The object-to-object detection results are obtained through overlay analysis, and the quantitative height change values are calculated according to this correspondence. The superiority of the proposed algorithm is that it can obtain the changes of buildings in planar and vertical simultaneously. The performance of the algorithm is evaluated in detail using six groups of satellite datasets. The experimental results prove the effectiveness of the proposed building change detection algorithm.

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Section: Introductionmentioning

confidence: 99%

Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery

Wang

Zhang

et al. 2022

Remote Sensing

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“…Although it performed better on eight small test areas, but its performance was limited by the training data set. On the other hand, Qin and Gruen [7] applied the support vector machine (SVM) classification method to aerial and DSM images for extracting the building segments. The segments were then used to detect the changes by analysing the correlation between the reference and modified data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Building Change Detection Method Using 3D Building Models

Siddiqui

Awrangjeb

2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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The traditional procedure for the building change detection is subjective to user's knowledge of the involved data. The complexity increases further due to unavailability of a public data set that is scanned on two different dates. Therefore, the manual changes in the reference data are more common to generate modified data. This paper first presents a strategy to introduce five types of changes in the reference data. Next, the proposed robust building change detection method runs analysis on planes' connectivity to represent the 3D building models of the reference and modified data. Based on the plane connection information, the 3D building models are compared for identification of the building changes and classify them into unchanged, demolished, new, modified, and partially-modified planes. The experimental results show that the proposed building change detection method successfully detects all introduced changes in the test data set.

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A volumetric change detection framework using UAV oblique photogrammetry – a case study of ultra-high-resolution monitoring of progressive building collapse

Huang

Song

et al. 2021

International Journal of Digital Earth

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A supervised method for object-based 3D building change detection on aerial stereo images

Cited by 4 publications

References 16 publications

Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery

Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery

A Novel Building Change Detection Method Using 3D Building Models

A volumetric change detection framework using UAV oblique photogrammetry – a case study of ultra-high-resolution monitoring of progressive building collapse

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