Automatic Detection of Elevation Changes by Differential DSM Analysis: Application to Urban Areas

Guérin, Cyrielle; Binet, Renaud; Pierrot-Deseilligny, Marc

doi:10.1109/jstars.2014.2300509

Cited by 27 publications

(19 citation statements)

References 33 publications

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“…The CD results highly depend on the classification accuracies that may require careful sample collection and feature design. (Sasagawa et al, 2008), (Fan et al, 1999;Liu et al, 2003;Pang et al, 2014), (Chaabouni-Chouayakh et al, 2010;Choi et al, 2009;Zhu et al, 2008), (Chaabouni-Chouayakh and Reinartz, 2011), (Guerin et al, 2014), (Qin, 2014b) , (Tian et al, 2014b), (Tian et al, 2014a) , (Nemmour and Chibani, 2006;Qin, 2014a;Trinder and Salah, 2012) (Olsen, 2004), (Walter, 2004), (Matikainen et al, 2010), (Champion, 2007), (Olsen and Knudsen, 2005), (Rottensteiner et al, 2007), (Qin et al, 2015a), , (Nebiker et al, 2014), (Champion et al, 2009),…”

Section: Change Detection Techniques With 3d Informationmentioning

confidence: 99%

“…Various shape features such as roughness, size, and height of the change segments were used to classify them into different types of objects. Based on the assumption that the change maps are globally smooth, Guerin et al (2014) applied a global optimization that employs this spatial context using a generalized dynamic programming to eliminate potential inaccuracies resulting from DSM subtractions. Markov random field as a powerful refinement model, were used in 2D CD approaches (Bruzzone and Prieto, 2002;Kasetkasem and Varshney, 2002), and similar methods were also developed under a 3D context (Pollard and Mundy, 2007;Qin and Gruen, 2014;Taneja et al, 2015).…”

Section: Post-refinementmentioning

confidence: 99%

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3D change detection – Approaches and applications

Qin

Tian

Reinartz

2016

ISPRS Journal of Photogrammetry and Remote Sensing

220

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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: Change Detection Techniques With 3d Informationmentioning

confidence: 99%

Section: Post-refinementmentioning

confidence: 99%

3D change detection – Approaches and applications

Qin

Tian

Reinartz

2016

ISPRS Journal of Photogrammetry and Remote Sensing

220

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“…The point clouds generated from DIM are mostly used for producing digital surface models (DSMs), which represents the elevation of the ground, as well all objects on the ground. The DSMs from at least two different dates can be successfully used in the change detection process by height comparison, and they can be generated not only from the aerial images, but also from satellite stereopairs (Guerin et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Approach for Detecting Changes With the Use of Different Pyramid Levels in Dense Image Matching

Pilarska

2020

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

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Abstract. Many cities order spatial data systematically, in particular aerial nadir images and orthophotomaps. However, only the orthoimages and orthophotomaps are usually used by the city administration, particularly in spatial planning. Some of the users are not aware of the possibilities as to how the aerial images can be used. Spatial data users, who may not be specialists in photogrammetry, are sometimes not aware that it is possible to obtain 3D information from 2D images as a point cloud. The idea of dense image matching (DIM) is well-known and described in the field of photogrammetry. Although dense image matching is a time- and memory-consuming process, this does not present a major drawback with modern computing. Images for the test area – Warsaw – are characterised by Ground Sampling Distance (GSD) equal to 8 cm. These images can be successfully used in change detection processes, comparing the dense image matching point cloud from two different dates. What is important while considering land cover change detection, is that it is not necessary to generate a detailed and high-density point cloud, e.g. in order to detect changes in buildings. The main idea of the article is to present the possibility of using higher levels of images pyramid in dense image matching within the change detection process as a way to optimize the processing time and point cloud accuracy. Which level of pyramid is needed to detect different changes in urban land cover will also be discussed.

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“…It provides a valuable source of information about the environment which can be applied in many areas of human activity such as change detection in rural landscape [1,2], solar radiation modeling [3][4][5], change detection in urban areas [6,7], urban planning [8], GNSS multipath prediction [9], sound propagation modeling [10], wind flow simulation [11], power-line corridors management [12] or visibility analysis [13]. The crucial issue for the application of the DSM is its reliability.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Redundancy of Multiple Overlapping Aerial Images for Dense Image Matching Based Digital Surface Model Generation

Dominik

2017

Remote Sensing

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Abstract:In recent years, significant development in the domain of dense image matching (DIM) can be observed. Meanwhile, in most countries, aerial images are acquired countrywide on a regular basis with decreasing time intervals and increasing image overlaps. Therefore, aerial images represent a growing potential for digital surface model (DSM) acquisition and updating. Surface reconstruction by image matching, in most cases, requires dealing with the redundancy caused by multiple overlapping images. Many approaches considering this redundancy in the surface reconstruction process have been developed. However, there is no commonly accepted procedure for this task. From the experience of the author, it can be stated that currently applied methods show some limitations regarding DSM generation from aerial images. Therefore, it is claimed that there is room for the development of new algorithms for integration of dense image matching results from multiple stereo pairs. Methods dedicated to aerial image based DSM generation that would exploit the specificity of this task are desirable. In this paper, an approach to compute the DSM elevations from redundant elevation hypotheses derived by pairwise dense image matching is presented. The proposed approach takes into account the base-to-height (b/h) ratio of stereo pairs, the distribution of elevation hypotheses from multiple stereo pairs and the neighboring elevations. An algorithm of selection of the elevation hypotheses used for the calculation of the final DSM elevation for each grid cell was developed. The algorithm was used to generate the DSM based on two sets of aerial images having significantly different acquisition parameters. The results were compared to the models obtained from several commonly used software packages for image based DSM generation. The quality assessment was carried out by visual inspection of terrain profiles and shaded surface display as well as by the planarity control of flat parts of the terrain. The assessment of the results showed that the application of the proposed algorithm can bring some advantages and it can contribute to improving the quality of the DSM.

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Automatic Detection of Elevation Changes by Differential DSM Analysis: Application to Urban Areas

Cited by 27 publications

References 33 publications

3D change detection – Approaches and applications

3D change detection – Approaches and applications

Hierarchical Approach for Detecting Changes With the Use of Different Pyramid Levels in Dense Image Matching

Exploiting the Redundancy of Multiple Overlapping Aerial Images for Dense Image Matching Based Digital Surface Model Generation

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