Change detection and primacy of the ground surface in scene organization

Z. Bian; G. J. Andersen

doi:10.1167/6.6.732

Geohazard Mapping and Identification Along Pipeline Right-of-Ways Using Space-Borne Synthetic Aperture Radar

Volume 2: Pipeline Integrity Management ◽

10.1115/ipc2012-90321 ◽

2012 ◽

Author(s):

Michael D. Henschel ◽

Gillian Robert ◽

Benjamin Deschamps ◽

Richard W. Gailing

Keyword(s):

Synthetic Aperture Radar ◽

Change Detection ◽

Ground Surface ◽

Synthetic Aperture ◽

Efficient Manner ◽

Joint Project ◽

Radar Imagery ◽

Satellite Radar ◽

Cost Efficient ◽

Aperture Radar

Many energy pipelines traverse hilly and mountainous terrains that are prone to landslides and other geohazards. The mapping, identification, and monitoring of geohazards along pipeline right-of-ways are essential in effectively managing the risks they may pose to pipeline integrity and human safety. One approach to monitoring potential geohazards is to use space-borne synthetic aperture radar (SAR). SAR is an effective and proven technology used to monitor ground surface change over vast areas and can be used in a cost-efficient manner, especially in areas that are remote or challenging. This paper focuses on aspects of a novel three-fold approach for monitoring geohazards using satellite radar imagery. This is accomplished by combining interferometric SAR (InSAR), automated amplitude change detection, and polarimetric change detection. The resulting analyses are to be merged into a new geohazard index map that will provide a simplified overview of the change influence for given pipeline segments over an extensive area. It is anticipated that the geohazard index map would be used to support operator decision-making in proactively mitigating the potential adverse effects identified. A brief introduction to the methodologies employed and a discussion of the validation that is currently underway as a joint project between MDA Geospatial Services and Southern California Gas Company is provided by this paper.

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The ground surface advantage in change detection: coherent surface structure

Journal of Vision ◽

10.1167/8.6.456 ◽

2010 ◽

Vol 8 (6) ◽

pp. 456-456

Author(s):

Z. Bian ◽

G. Andersen

Keyword(s):

Change Detection ◽

Surface Structure ◽

Ground Surface

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Deep Siamese Networks Based Change Detection with Remote Sensing Images

Remote Sensing ◽

10.3390/rs13173394 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3394 ◽

Cited By ~ 1

Author(s):

Le Yang ◽

Yiming Chen ◽

Shiji Song ◽

Fan Li ◽

Gao Huang

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Change Detection ◽

Ground Surface ◽

Semantic Segmentation ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Image Pairs ◽

Segmentation Task ◽

High Level

Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.

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Vivid Imagers Are Better at Detecting Salient Changes

Journal of Individual Differences ◽

10.1027/1614-0001.27.4.218 ◽

2006 ◽

Vol 27 (4) ◽

pp. 218-228 ◽

Cited By ~ 13

Author(s):

Paul Rodway ◽

Karen Gillies ◽

Astrid Schepman

Keyword(s):

Individual Differences ◽

Change Detection ◽

Visual Imagery ◽

Detection Task ◽

Level Of Detail ◽

Detection Accuracy ◽

Change Detection Task ◽

Term Change ◽

High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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