Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake

Putri, Ade Febri Sandhini; Widyatmanti, Wirastuti; Umarhadi, Deha Agus

doi:10.1016/j.rsase.2022.100724

Cited by 11 publications

(11 citation statements)

References 24 publications

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“…The SAR intensity change results more favorably highlighted the significant backscatter changes (severe damage) paired with larger building footprints than the small changes (moderate and possibly damage). This is also consistent with the previous research results that it is challenging to detect lower damage grades from the medium resolution images [3,21,51]. Additionally, there were 23 damaged buildings, as shown by the green polygons in Figure 9, that were detected by the SAR intensity analysis, but were not included in the UNOSAT damage assessment report.…”

Section: Discussionsupporting

confidence: 91%

“…Texture in optical images describes the spatial variation of the brightness intensity of the neighboring pixels, thus the brightness intensity of the dark roof building and surrounding areas shows lower brightness intensity, and the change is not significant on both pre-and post-event optical images. Previous researchers also found that the damage classification accuracy would be compromised when the roof materials are diverse [21]. Further research is needed to evaluate their performance in different areas, especially because there are contradictory research results.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the GLCM Mean was chosen as a texture index for differentiating a damaged building from an undamaged building. The GLCM Mean is a useful feature that may contribute to distinguishing individual class signatures for landscape classification, especially in building damage classification [21]. It is an interior texture, which is defined as a metric yielding high values for a neighborhood that contains few coherent edges but has many subtle and irregular variations [56].…”

Section: Optical Texture Analysismentioning

confidence: 99%

“…Furthermore, optical and SAR data are increasingly being employed in tandem to test different methods that augment the benefits of each sensor type [20]. In their study, Putri et al, integrated Sentinel-1 (SAR) and Sentinel-2 (optical) data using a random forest classifier to map the damaged buildings following the catastrophic 2018 Lombok Earthquake in Indonesia [21]. Adriano et al, used multi-source data fusion of optical (Sentinel-2 and Planet Scope) and SAR (Sentinel-1 and ALOS-2/PALSAR-2) images for building damage mapping of the 2018 Sulawesi Earthquake and Tsunami, also in Indonesia [22].…”

Section: Introductionmentioning

confidence: 99%

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War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

et al. 2022

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Natural and anthropogenic disasters can cause significant damage to urban infrastructure, landscape, and loss of human life. Satellite based remote sensing plays a key role in rapid damage assessment, post-disaster reconnaissance and recovery. In this study, we aim to assess the performance of Sentinel-1 and Sentinel-2 data for building damage assessment in Kyiv, the capital city of Ukraine, due to the ongoing war with Russia. For damage assessment, we employ a simple and robust SAR log ratio of intensity for the Sentinel-1, and a texture analysis for the Sentinel-2. To suppress changes from other features and landcover types not related to urban areas, we construct a mask of the built-up area using the OpenStreetMap building footprints and World Settlement Footprint (WSF), respectively. As it is difficult to get ground truth data in the ongoing war zone, a qualitative accuracy assessment with the very high-resolution optical images and a quantitative assessment with the United Nations Satellite Center (UNOSAT) damage assessment map was conducted. The results indicated that the damaged buildings are mainly concentrated in the northwestern part of the study area, wherein Irpin, and the neighboring towns of Bucha and Hostomel are located. The detected building damages show a good match with the reference WorldView images. Compared with the damage assessment map by UNOSAT, 58% of the damaged buildings were correctly classified. The results of this study highlight the potential offered by publicly available medium resolution satellite imagery for rapid mapping damage to provide initial reference data immediately after a disaster.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Optical Texture Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

et al. 2022

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“…It consists of two orbiting satellites and collects multispectral images at resolutions of 10/20/60 m in specific wavelength bands [13]. It provides medium-resolution multispectral Earth observation imagery for various applications [14][15][16][17]. The satellite data can be accessed via the European Space Agency's Copernicus Data Hub, ensuring access to up-to-date and reliable information for researchers and practitioners alike.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Spatiotemporal Fusion Architecture of Landsat 8 and Sentinel-2 Data for 10 m Series Imagery

Cheng,

Xie,

et al. 2024

Remote Sensing

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Medium- to high-resolution imagery is indispensable for various applications. Combining images from Landsat 8 and Sentinel-2 can improve the accuracy of observing dynamic changes on the Earth’s surface. Many researchers use Sentinel-2 10 m resolution data in conjunction with Landsat 8 30 m resolution data to generate 10 m resolution data series. However, current fusion techniques have some algorithmic weaknesses, such as simple processing of coarse or fine images, which fail to extract image features to the fullest extent, especially in rapidly changing land cover areas. Facing the aforementioned limitations, we proposed a multiscale and attention mechanism-based residual spatiotemporal fusion network (MARSTFN) that utilizes Sentinel-2 10 m resolution data and Landsat 8 15 m resolution data as auxiliary data to upgrade Landsat 8 30 m resolution data to 10 m resolution. In this network, we utilized multiscale and attention mechanisms to extract features from coarse and fine images separately. Subsequently, the features outputted from all input branches are combined and further feature information is extracted through residual networks and skip connections. Finally, the features obtained from the residual network are merged with the feature information of the coarsely processed images from the multiscale mechanism to generate accurate prediction images. To assess the efficacy of our model, we compared it with existing models on two datasets. Results demonstrated that our fusion model outperformed baseline methods across various evaluation indicators, highlighting its ability to integrate Sentinel-2 and Landsat 8 data to produce 10 m resolution data.

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A novel image fusion-based post classification framework for agricultural variations detection using Sentinel-1 and Sentinel-2 data

Vyas,

Singh,

Sethi

2024

Earth Sci Inform

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Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake

Cited by 11 publications

References 24 publications

War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

Deep Learning-Based Spatiotemporal Fusion Architecture of Landsat 8 and Sentinel-2 Data for 10 m Series Imagery

A novel image fusion-based post classification framework for agricultural variations detection using Sentinel-1 and Sentinel-2 data

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