Assessing Buildings Damage from Multi-Temporal Sar Images Fusion using Semantic Change Detection

Pang, Lei; Zhang, Fengli; Li, Lü; Huang, Qiqi; Jiao, Yanan; Yun, Shao

doi:10.1109/igarss46834.2022.9884915

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Pang et al [106] presented a method for building damage assessment using semantic change detection on high-resolution SAR images. Their model leverages a Siamese-based module for damage change detection and an attention mechanism-based module for semantic segmentation of the damage map.…”

Section: Sar Sample-based Methodsmentioning

confidence: 99%

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu,

Xie,

Zhang

et al. 2024

Buildings

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After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner’s lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms.

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Section: Sar Sample-based Methodsmentioning

confidence: 99%

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu,

Xie,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…In a wide range of applications, change detection using multitemporal remote sensing imaging is crucial. Examples include urbanization [ 26 ], deforestation [ 27 ], flooding [ 28 ], infrastructure monitoring [ 29 ], disaster monitoring, and damage assessment [ 30 ]. Reference [ 14 ], however, there are only a few publications on the use of SAR image change detection in the military and security—here, an example can be given Novak [ 16 ] or Canty [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence

Slesinski,

Wierzbicki,

Kedzierski

2023

Sensors

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Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.

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Assessing Buildings Damage from Multi-Temporal Sar Images Fusion using Semantic Change Detection

Cited by 2 publications

References 14 publications

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence

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