2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

Gstaiger, Veronika; Tian, Jiaojiao; Kiefl, Ralph; Kurz, Franz

doi:10.3390/rs10122054

Cited by 8 publications

(6 citation statements)

References 40 publications

(58 reference statements)

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“…In [109], the authors present an interesting approach to change detection that incorporates both 2D and 3D information. The 3D information refers to terrain information.…”

Section: Supervisedmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

Information

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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“…In [109], the authors present an interesting approach to change detection that incorporates both 2D and 3D information. The 3D information refers to terrain information.…”

Section: Supervisedmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

Information

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“…Considering that vehicles are significantly higher than the ground, the available DSM can provide very useful height information to separate vehicles from background. The DSM we used has been generated with the same approach as described in [12]. In addition, spectral information and elevation information will be fused and encoded for the separation of objects and the ground, as well as the subsequent acquisition of object location information.…”

Section: Auto-tagging Label Generationmentioning

confidence: 99%

“…This dataset used in the paper is a subset from the aerial imagery of a camping site in northern Germany [12]. It was acquired by the optical 4K camera system on the German Aerospace Center (DLR) research helicopter BO 105 (DLR (CC-BY 3.0)) at a height of 600 m above the ground.…”

Section: Aerial Imagerymentioning

confidence: 99%

“…With three cameras on board, the 4K camera system is able to capture the multi-view imagery with 90% overlap along-track and 60% overlap across the track. The DSMs were then prepared using the automatic processing chain as introduced in [12]. Table 1 lists the quantitative results using F-RCN trained by DOTA dataset and F-RCN fine-tuned by the target dataset with an auto-tagging label.…”

Section: Aerial Imagerymentioning

confidence: 99%

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A Weakly-Supervised Deep Network for DSM-Aided Vehicle Detection

Hong

Tian

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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With the breakthrough of the spatial resolution of optical remote sensing images at the sub-meter level and the explosive development of deep learning, geospatial object detection has achieved a growing interest in remote sensing community. However, labeling large training datasets in object level is still an expensive and tedious procedure. This might lead to the poor model generalization and degraded network learning ability. To this end, a weakly-supervised deep network (WSDN) is developed for geospatial object detection by applying a digital surface model (DSM)-aided auto-labeling and a pre-trained network learned from the task-independent dataset. Experimental results conducted on the stereo aerial imagery of a large camping site are performed to demonstrate that the proposed WSDN yields better detection results, with 62.78% precision and 55.13% recall.

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“…For 3D change detection, Qin et al [4] summarized the existing 3D change detection algorithms in detail. Gstaiger et al [13] concluded that using 3D change information combined with 2D information for change detection can deliver valuable indications. For the pixel-based method, Tian et al [14,15] used digital surface models (DSM) difference and threshold segmentation to detect building change.…”

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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2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

Cited by 8 publications

References 40 publications

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

A Weakly-Supervised Deep Network for DSM-Aided Vehicle Detection

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

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