An Application of Segnet for Detecting Landslide Areas by Using Fully Polarimetric Sar Data

Antara, I Made Oka Guna; Shimizu, Norikazu; Osawa, Takahiro; Nuarsa, I Wayan

doi:10.24843/ejes.2019.v13.i02.p09

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…The architecture of SegNet is illustrated in Figure 6, which consists of an encoder portion and a corresponding decoder portion [75]. The encoder portion is used to train the segmentation engine, while the decoder portion is used to obtain pixel-wise classification [74,76]. Each encoder contains 3 × 3 convolution layer followed by a batch normalization layer and a ReLU activation function to produce a set of feature maps.…”

Section: Segnet Modelmentioning

confidence: 99%

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Liang,

Zhang,

et al. 2023

Remote Sensing

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Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management.

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Section: Segnet Modelmentioning

confidence: 99%

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Liang,

Zhang,

et al. 2023

Remote Sensing

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“…This is influenced by various factors, including surface features and composition, topography, land use, and moisture and water content [32]. The scattering characteristics of different surface features and materials, variations in topography and land use types, and the presence of moisture and water content all affect the polarization response of SAR data, influencing the ability to identify landslides [33]. Considering these differences and the specific conditions of the study area, including surface features, detection requirements, and data availability, it is crucial to select appropriate polarization modes for research and analysis.…”

Section: Introductionmentioning

confidence: 99%

Fast Mapping of Large-Scale Landslides in Sentinel-1 SAR Images Using SPAUNet

Shi,

Wu,

Guo

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rapid and accurate mapping of large-scale landslides is crucial for post-disaster prevention and risk analysis. This research introduces a new method named SPAUNet to address the challenges of noise and information redundancy when utilizing Synthetic Aperture Radar (SAR) amplitude data for landslide detection. This process is based on constructing a UNet model and incorporating a pixel attention mechanism. This study uses pre-and post-disaster SAR amplitude images as input data, generates seven pairs of dualband combinations through registration, and inputs them into the model in sequence to automatically explore the best polarization amplitude combination, achieving rapid per-pixel mapping of largescale landslides. Empirical analysis of landslide events in Milan and Papua New Guinea shows that SPAUNet outperforms the baseline models, improving the F1 score by 17% in Milan and 19% in Papua New Guinea. Moreover, this study emphasizes the importance of choosing the appropriate polarization combination for the region. The results indicate that SPAUNet, along with the appropriate polarization amplitude combination, improves the accuracy and reliability of landslide mapping, aiding disaster assessment and recovery work. This improvement holds significant implications for landslide disaster assessment and post-disaster recovery, providing a valuable direction for further enhancing landslide monitoring capabilities.

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“…2023, 15, 5619 3 of 25 NAVA et al [33] made the first attempt to combine VV-polarized SAR amplitude images with the UNet model for landslide identification, showcasing the potential of model predictions combined with SAR amplitude images for landslide identification tasks. However, SAR amplitude images contain complex noise and redundancy, and they can introduce significant distortions when capturing steep terrain areas [34][35][36]. Due to side-looking radar satellite imaging characteristics, SAR images may exhibit geometric distortions, including foreshortening, layover, and shadow, affecting landslide recognition and shape analysis [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition

Pan,

Shi

2023

Remote Sensing

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Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities.

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An Application of Segnet for Detecting Landslide Areas by Using Fully Polarimetric Sar Data

Cited by 8 publications

References 19 publications

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Fast Mapping of Large-Scale Landslides in Sentinel-1 SAR Images Using SPAUNet

Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition

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