Extraction of Collapsed Bridges Due to the 2011 Tohoku-Oki Earthquake from Post-Event SAR Images

2018 ◽  
Vol 13 (2) ◽  
pp. 281-290 ◽  
Author(s):  
Wen Liu ◽  
◽  
Fumio Yamazaki
Keyword(s):  
Emergency Response ◽  
Synthetic Aperture ◽  
Threshold Values ◽  
Visual Interpretation ◽  
Sar Images ◽  
Optical Images ◽  
Gis Data ◽  
Intensity Image ◽  
Effective Use ◽  
Iwate Prefecture

Since synthetic aperture radar (SAR) sensors onboard satellites can work under all weather and sunlight conditions, they are suitable for information gathering in emergency response after disasters occur. This study attempted to extract collapsed bridges in Iwate Prefecture, Japan, which was affected by more than 15-m high tsunamis due to the Mw 9.0 earthquake on March 11, 2011. First, the locations of the bridges were extracted using GIS data of roads and rivers. Then, we attempted to detect the collapsed or washed-away bridges using visual interpretation and thresholding methods. The threshold values on the SAR backscattering coefficients and the percentage of non-water regions were applied to the post-event high-resolution TerraSAR-X images. The results were compared with the optical images and damage investigation reports. The effective use of a single SAR intensity image in the extraction of collapsed bridges was demonstrated with a high overall accuracy of more than 90%.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

scholarly journals Evidence of Instability in Previously-Mapped Landslides as Measured Using GPS, Optical, and SAR Data between 2007 and 2017: A Case Study in the Portuguese Bend Landslide Complex, California

2019 ◽  
Vol 11 (8) ◽  
pp. 937 ◽  
Author(s):  
El Hachemi Bouali ◽  
Thomas Oommen ◽  
Rüdiger Escobar-Wolf
Keyword(s):  
Synthetic Aperture ◽  
Gps Data ◽  
Persistent Scatterer Interferometry ◽  
Sar Images ◽  
Data Synthesis ◽  
Optical Images ◽  
Persistent Scatterer ◽  
Temporal Coverage ◽  
Displacement Measurements

Velocity dictates the destructive potential of a landslide. A combination of synthetic aperture radar (SAR), optical, and GPS data were used to maximize spatial and temporal coverage to monitor continuously-moving portions of the Portuguese Bend landslide complex on the Palos Verdes Peninsula in Southern California. Forty SAR images from the COSMO-SkyMed satellite, acquired between 19 July 2012 and 27 September 2014, were processed using Persistent Scatterer Interferometry (PSI). Eight optical images from the WorldView-2 satellite, acquired between 20 February 2011 and 16 February 2016, were processed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) technique. Displacement measurements were taken at GPS monuments between September 2007 and May 2017. Incremental and average deformations across the landslide complex were measured using all three techniques. Velocity measured within the landslide complex ranges from slow (> 1.6 m/year) to extremely slow (< 16 mm/year). COSI-Corr and GPS provide detailed coverage of m/year-scale deformation while PSI can measure extremely slow deformation rates (mm/year-scale), which COSI-Corr and GPS cannot do reliably. This case study demonstrates the applicability of SAR, optical, and GPS data synthesis as a complimentary approach to repeat field monitoring and mapping to changes in landslide activity through time.

scholarly journals Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

2020 ◽  
pp. 147-156
Author(s):  
Freskida Abazaj ◽  
Gëzim Hasko
Keyword(s):  
European Space Agency ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
Space Agency ◽  
Optical Images ◽  
River Region ◽  
Speckle Filtering ◽  
Flooded Areas

Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images. In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA). Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

scholarly journals SC-SM CAM: An Efficient Visual Interpretation of CNN for SAR Images Target Recognition

2021 ◽  
Vol 13 (20) ◽  
pp. 4139
Author(s):  
Zhenpeng Feng ◽  
Hongbing Ji ◽  
Ljubiša Stanković ◽  
Jingyuan Fan ◽  
Mingzhe Zhu
Keyword(s):  
Spectral Clustering ◽  
Target Recognition ◽  
Efficient Computation ◽  
Visual Interpretation ◽  
Feature Maps ◽  
Sar Images ◽  
Optical Images ◽  
The Cost ◽  
The Relationship ◽  
Activation Mapping

Convolutional neural networks (CNNs) have successfully achieved high accuracy in synthetic aperture radar (SAR) target recognition; however, the intransparency of CNNs is still a limiting or even disqualifying factor. Therefore, visually interpreting CNNs with SAR images has recently drawn increasing attention. Various class activation mapping (CAM) methods are adopted to discern the relationship between CNN’s decision and image regions. Unfortunately, most existing CAM methods are based on optical images; thus, they usually lead to a limiting visualization effect for SAR images. Although a recently proposed Self-Matching CAM can obtain a satisfactory effect for SAR images, it is quite time-consuming, due to there being hundreds of self-matching operations per image. G-SM-CAM reduces the time of such operation dramatically, but at the cost of visualization effect. Based on the limitations of the above methods, we propose an efficient method, Spectral-Clustering Self-Matching CAM (SC-SM CAM). Spectral clustering is first adopted to divide feature maps into groups for efficient computation. In each group, similar feature maps are merged into an enhanced feature map with more concentrated energy in a specific region; thus, the saliency heatmaps may more accurately tally with the target. Experimental results demonstrate that SC-SM CAM outperforms other SOTA CAM methods in both effect and efficiency.

scholarly journals FLOOD DETECTION IN TIME SERIES OF OPTICAL AND SAR IMAGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1343-1346
Author(s):  
C. Rambour ◽  
N. Audebert ◽  
E. Koeniguer ◽  
B. Le Saux ◽  
M. Crucianu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Flood Events ◽  
Sar Images ◽  
Optical Images ◽  
Flood Detection ◽  
Network Approaches

Abstract. These last decades, Earth Observation brought a number of new perspectives from geosciences to human activity monitoring. As more data became available, Artificial Intelligence (AI) techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. To help the community go forward, we introduce a new dataset composed of co-registered optical and SAR images time series for the detection of flood events and new neural network approaches to leverage these two modalities.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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