A New Automated Hierarchical Clustering Algorithm Based on Emergent Self Organizing Maps

Moosavi, Seyed Vahid; Qin, Rongjun

doi:10.1109/iv.2012.52

Cited by 2 publications

(1 citation statement)

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“…The object map was fused with the height difference map to obtain four types of changes. Under a 3D model updating process, Qin (2014a) fused multiple change evidence resulting from DSM and spectral features via unsupervised self-organizing maps (SOM) (Kohonen, 1982;Moosavi and Qin, 2012), where the a priori information (the quality of the change evidence) can be used to weight individual change indicators to obtain the final change evidence for change determination.…”

Section: Direct Feature Fusionmentioning

confidence: 99%

3D change detection – Approaches and applications

Qin

Tian

Reinartz

2016

ISPRS Journal of Photogrammetry and Remote Sensing

223

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Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD.

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Section: Direct Feature Fusionmentioning

confidence: 99%