A segmentation network with multiattention and its application to SAR image analysis

An, Yongli; Long, Jianwu; Mabu, Shingo

doi:10.1002/tee.23090

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…width and revisit cycle are not signi cant and observation modes switch frequently Ohki et al (2020). andAn et al (2020) used pre-and post-disaster ALOS-2 images and machine learning methods to achieve higher detection accuracy than our results (Case3 in this study), which accompanied many small debris ows in vegetated mountainous areas. In addition,Ohki et al.…”

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confidence: 59%

Landslide detection by deep learning based semantic segmentation using Sentinel-1 intensity images

Ito

Kinoshita

2023

Preprint

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Fast-moving landslides associated with earthquakes and/or heavy rains can cause significant human and socioeconomic damage. Satellite synthetic aperture radar (SAR) can observe wide areas regardless of the presence of clouds and sunlight, and thus is a promising tool for detecting landslides immediately after a disaster occurrence. Recently, the application of deep learning-based semantic segmentation to satellite optical imagery is active for the landslide detection, but its application to SAR imagery is still limited. Here we investigated landslide detection capability of deep learning-based semantic segmentation using pre- and post-disaster Sentinel-1 SAR intensity images. We used U-Net as the deep learning model and validated the method on four disaster cases composed of two earthquake and two heavy rain events. In the cases of the 2018 Hokkaido Eastern Iburi earthquake and the July 2017 Northern Kyushu heavy rainfall, detection models trained in the same single case or on multiple cases could identify large landslides and spatial concentrations of damage areas. The model trained in the 2018 Hokkaido Eastern Iburi earthquake provided the highest Kappa coefficient (0.624) when we applied it to the same case. The proposed method could detect landslides that had not been detected by emergency aerial observation, demonstrating the effectiveness of the combination of satellite SAR and deep learning for the landslide detection. However, the detection performance of trained models was degraded when they were applied to cases with different characteristics in terms of topography, vegetation and landslide occurrence mechanisms from training cases. Therefore, our results indicated that detection models should be trained in various cases for improving versatility. For future practical use, further validations by other disaster cases using various kinds of satellite SARs are needed.

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Landslide detection by deep learning based semantic segmentation using Sentinel-1 intensity images

Ito

Kinoshita

2023

Preprint

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“…SAR images with a lot of noise and lack of texture and color information will be affected a lot. Using traditional image segmentation methods will result in poor segmentation effects 7 . With the emergence of deep learning networks, its powerful feature extraction capabilities have been verified in many fields.…”

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confidence: 99%

Precise segmentation of offshore farms in high-resolution SAR images based on improved UNet++

Liu

Xia

2022

International Conference on Computer Application and Information Security (ICCAIS 2021)

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The segmentation of the offshore farm area in the high-resolution SAR image is of great significance for the statistics of the farming area and the analysis of the rationality of the farming layout. However, the SAR images have the characteristics of a lot of noise and inconspicuous features. It is difficult to achieve precise segmentation by directly using non-learning image segmentation methods. Therefore, we propose a precise segmentation scheme for offshore farms in high-resolution SAR images based on improved UNet++. We first adopt a simulated annealing strategy for the update of the learning rate during the network training. By initializing the learning rate multiple times, we avoid the network from falling into a local optimum. Secondly, for the data set studied, we verify that the segmentation performance of resizing the image to 256×256 pixels is better than that of 512×512 pixels. Finally, we propose an improved UNet++, which uses SE-Net as the feature extraction network to enhance the feature learning ability. Extensive experimental results show that, compared to some state-of-the-art methods, the proposed scheme achieves superior performance with a Frequency Weighted Intersection over Union (FWIoU) of 0.9853 on the high-resolution SAR offshore farm data set.

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Deep learning for geological hazards analysis: Data, models, applications, and opportunities

Mei

2021

Earth-Science Reviews

149

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A segmentation network with multiattention and its application to SAR image analysis

Cited by 4 publications

References 22 publications

Landslide detection by deep learning based semantic segmentation using Sentinel-1 intensity images

Landslide detection by deep learning based semantic segmentation using Sentinel-1 intensity images

Precise segmentation of offshore farms in high-resolution SAR images based on improved UNet++

Deep learning for geological hazards analysis: Data, models, applications, and opportunities

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