Integrating Unordered Time Frames in Neural Networks: Application to the Detection of Natural Oil Slicks in Satellite Images

Scardigli, Antoine; Risser, Laurent; Haddouche, Chihab; Jatiault, Romain

doi:10.1109/tgrs.2023.3241681

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…Currently, there are four main approaches for landslide recognition methods based on remote sensing images: visual interpretation [13,14], pixel-based methods [15], objectbased methods [16], and methods based on deep learning (DL) techniques [17][18][19]. Visual interpretation refers to the manual annotation and classification of landslide areas in images by professionals by directly observing and analyzing the features, morphology, color, texture, and other information of remote sensing images [20,21].…”

Section: Introductionmentioning

confidence: 99%

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

Xi,

Yu,

et al. 2024

Remote Sensing

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Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently proposed segment anything model (SAM) has shown strong generalization capabilities in zero-shot semantic segmentation. Nevertheless, SAM heavily relies on user-provided prompts, and performs poorly in identifying landslides on remote sensing images. In this study, we propose a SAM-based cross-feature fusion network (SAM-CFFNet) for the landslide identification task. The model utilizes SAM’s image encoder to extract multi-level features and our proposed cross-feature fusion decoder (CFFD) to generate high-precision segmentation results. The CFFD enhances landslide information through fine-tuning and cross-fusing multi-level features while leveraging a shallow feature extractor (SFE) to supplement texture details and improve recognition performance. SAM-CFFNet achieves high-precision landslide identification without the need for prompts while retaining SAM’s robust feature extraction capabilities. Experimental results on three open-source landslide datasets show that SAM-CFFNet outperformed other comparative models in terms of landslide identification accuracy and achieved an intersection over union (IoU) of 77.13%, 55.26%, and 73.87% on the three datasets, respectively. Our ablation studies confirm the effectiveness of each module designed in our model. Moreover, we validated the justification for our CFFD design through comparative analysis with diverse decoders. SAM-CFFNet achieves precise landslide identification using remote sensing images, demonstrating the potential application of the SAM-based model in geohazard analysis.

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Section: Introductionmentioning

confidence: 99%

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

Xi,

Yu,

et al. 2024

Remote Sensing

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“…SAR technology is a remote sensing technique that uses radar to transmit and receive electromagnetic waves for imaging. It offers all-weather and all-day imaging capabilities and can detect Earth's surface with high resolution [21]. Compared to optical satellites, SAR satellites are less sensitive to weather and cloud cover, providing stable performance even in adverse weather conditions [22,23].…”

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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“…To expedite landslide mapping, researchers have focused on utilizing Synthetic Aperture Radar (SAR) satellite imagery, which possesses cloud-penetrating capabilities, for landslide identification studies. SAR technology uses radar waves for imaging, offering all-weather and all-day imaging capabilities and high-resolution imaging of the Earth's surface [17,18].…”

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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Integrating Unordered Time Frames in Neural Networks: Application to the Detection of Natural Oil Slicks in Satellite Images

Cited by 5 publications

References 49 publications

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

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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