Object-Based Method for Estimating Tsunami-Induced Damage Using TerraSAR-X Data

2016 ◽  
Vol 11 (2) ◽  
pp. 225-235 ◽  
Author(s):  
Hideomi Gokon ◽  
◽  
Shunichi Koshimura ◽  
Masashi Matsuoka ◽  
◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Tohoku Earthquake ◽  
Ground Truth ◽  
Test Site ◽  
Damage Function ◽  
Building Damage ◽  
The 2011 Tohoku Earthquake ◽  
Ground Truth Data ◽  
Object Based ◽  
The Relationship

The object-based method we developed to estimate building damage uses high-resolution synthetic aperture radar (TerraSAR-X) data from the 2011 Tohoku earthquake and tsunami. The damage function we developed involves the relationship between changes in the sigma nought values of pre- and postevent TerraSAR-X data and the damage ratio of washed-away buildings. We confirmed that the function performed as expected by estimating the number of washed-away buildings in homogeneous areas, agreeing well with ground truth data verified by a Pearson fs correlation coefficient of 0.99. The same damage function applied at another test site yielded a Pearson's correlation coefficient of 0.98. These results are sufficient to ensure transferability. We then simplified and semiautomated these processes in an ArcGIS environment, estimating building damage in the city of Sendai within 26 minutes.

Damage Detection Using High-Resolution SAR Imagery in the 2009 L'Aquila, Italy, Earthquake

2013 ◽  
Vol 29 (4) ◽  
pp. 1521-1535 ◽  
Author(s):  
Pralhad Uprety ◽  
Fumio Yamazaki ◽  
Fabio Dell'Acqua
Keyword(s):  
High Resolution ◽  
Damage Detection ◽  
Correlation Coefficient ◽  
Ground Truth ◽  
Threshold Value ◽  
Building Damage ◽  
Urban Setting ◽  
Ground Truth Data ◽  
Emergency Situations ◽  
Sar Imagery

Satellite remote sensing is being used to monitor disaster-affected areas for post-disaster reconnaissance and recovery. One of the special features of Synthetic Aperture Radar (SAR) is that it can operate day and night and penetrate the cloud cover because of which it is being widely used in emergency situations. Building damage detection for the 6 April 2009 L'Aquila, Italy, earthquake was conducted using high-resolution TerraSAR-X images obtained before and after the event. The correlation coefficient and the difference of backscatter coefficients of the pre- and post-event images were calculated in a similar way as Matsuoka and Yamazaki (2004) . The threshold value of the correlation coefficient was suggested and used in detecting building damage. The results were compared with ground truth data and a post-event optical image. Based on the study, building damage could be observed in an urban setting of L'Aquila with overall accuracy of 89.8% and Kappa coefficient of 0.45.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

Detecting building damage caused by the 2011 Tohoku earthquake tsunami using TerraSAR-X data

2014 ◽  
Author(s):  
Hideomi Gokon ◽  
Shunichi Koshimura ◽  
Joachim Post ◽  
Christian Geis ◽  
Enrico Stein ◽  
...  
Keyword(s):  
Tohoku Earthquake ◽  
Building Damage ◽  
2011 Tohoku Earthquake ◽  
The 2011 Tohoku Earthquake

scholarly journals Estimation of sea-ice physical parameters using polarimetric SAR: results from Okhotsk and Lake Saroma campaign

2001 ◽  
Vol 33 ◽  
pp. 120-124 ◽  
Author(s):  
Hiroyuki Wakabayashi ◽  
Takeshi Matsuoka ◽  
Kazuki Nakamura ◽  
Fumihiko Nishio
Keyword(s):  
Sea Ice ◽  
Correlation Coefficient ◽  
Ground Truth ◽  
Open Water ◽  
Physical Parameters ◽  
Dual Frequency ◽  
Ground Truth Data ◽  
National Space ◽  
Airborne Sar ◽  
Interferometric Sar

AbstractWe have acquired ground-truth data at Lake Saroma, northeast Hokkaido, Japan, and the surrounding area since 1993 in order to collect data on regional sea ice in the Sea of Okhotsk. The data were acquired in 1999 by polarimetric and interferometric SAR (Pi-SAR), the dual-frequency, fully polarimetric airborne SAR system jointly developed by the National Space Development Agency of Japan (NASDA) and the Communications Research Laboratory (CRL), simultaneously with ground experiments. This paper describes the results of polarimetric data analysis of typical sea ice observed in the offshore region near Lake Saroma. The polarimetric parameters used were correlation coefficient and phase difference. Based on the analysis of these parameters, we found that the correlation coefficient between RR and LL polarizations can discriminate four categories including three types of ice and open water.

scholarly journals Monitoring the Spring Flood in Lena Delta with Hydrodynamic Modeling Based on SAR Satellite Products

2021 ◽  
Vol 13 (22) ◽  
pp. 4695
Author(s):  
Avi Putri Pertiwi ◽  
Achim Roth ◽  
Timo Schaffhauser ◽  
Punit Kumar Bhola ◽  
Felix Reuß ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ground Truth ◽  
Test Site ◽  
Hydrodynamic Modeling ◽  
The Arctic ◽  
Spring Flood ◽  
Satellite Mission ◽  
Ground Truth Data ◽  
Lena Delta ◽  
Flow Velocities

Due to the remote location and the extreme climate, monitoring stations in Arctic rivers such as Lena in Siberia have been decreasing through time. Every year, after a long harsh winter, the accumulated snow on the Lena watershed melts, leading to the major annual spring flood event causing heavy transport of sediments, organic carbon, and trace metals, both into as well as within the delta. This study aims to analyze the hydrodynamic processes of the spring flood taking place every year in the Lena Delta. Thus, a combination of remote sensing techniques and hydrodynamic modeling methodologies is used to overcome limitations caused by missing ground-truth data. As a test site for this feasibility study, the outlet of the Lena River to its delta was selected. Lena Delta is an extensive wetland spanning from northeast Siberia into the Arctic Ocean. Spaceborne Synthetic Aperture Radar (SAR) data of the TerraSAR-X/TanDEM-X satellite mission served as input for the hydrodynamic modeling software HEC-RAS. The model resulted in inundation areas, flood depths, and flow velocities. The model accuracy assessed by comparing the multi-temporal modeled inundation areas with the satellite-derived inundation areas ranged between 65 and 95%, with kappa coefficients ranging between 0.78 and 0.97, showing moderate to almost perfect levels of agreement between the two inundation boundaries. Modeling results of high flow discharges show a better agreement with the satellite-derived inundation areas compared to that of lower flow discharges. Overall, the remote-sensing-based hydrodynamic modeling succeeded in indicating the increase and decrease in the inundation areas, flood depths, and flow velocities during the annual flood events.

scholarly journals VEGETATION COVER MAPPING BASED ON REMOTE SENSING AND DIGITAL ELEVATION MODEL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 699-704 ◽  
Author(s):  
M. A. Korets ◽  
V. A. Ryzhkova ◽  
I. V. Danilova ◽  
A. S. Prokushkin
Keyword(s):  
Digital Elevation Model ◽  
Boreal Forests ◽  
Forest Cover ◽  
Ground Truth ◽  
Test Site ◽  
Ground Truth Data ◽  
Digital Elevation ◽  
Forest Cover Mapping ◽  
Elevation Model ◽  
Digital Elevation Model Data

An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m<sup>2</sup>). The proposed approach was applied for the test site area (~3600 km<sup>2</sup>), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.

scholarly journals Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network

2020 ◽  
Vol 12 (15) ◽  
pp. 2345 ◽  
Author(s):  
Ahram Song ◽  
Yongil Kim ◽  
Youkyung Han
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Images ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Learning Network ◽  
Object Based ◽  
Deep Learning Network ◽  
Very High

Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with unsupervised CD methods and a CD network subjected to postprocessing by effectively utilizing the deep learning technique and object information.

scholarly journals VEGETATION COVER MAPPING BASED ON REMOTE SENSING AND DIGITAL ELEVATION MODEL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 699-704 ◽  
Author(s):  
M. A. Korets ◽  
V. A. Ryzhkova ◽  
I. V. Danilova ◽  
A. S. Prokushkin
Keyword(s):  
Digital Elevation Model ◽  
Boreal Forests ◽  
Forest Cover ◽  
Ground Truth ◽  
Test Site ◽  
Ground Truth Data ◽  
Digital Elevation ◽  
Forest Cover Mapping ◽  
Elevation Model ◽  
Digital Elevation Model Data

An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m&lt;sup&gt;2&lt;/sup&gt;). The proposed approach was applied for the test site area (~3600 km&lt;sup&gt;2&lt;/sup&gt;), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.

scholarly journals Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets

2020 ◽  
Vol 12 (24) ◽  
pp. 4055
Author(s):  
Yanbing Bai ◽  
Junjie Hu ◽  
Jinhua Su ◽  
Xing Liu ◽  
Haoyu Liu ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
High Performance ◽  
Large Scale ◽  
Tohoku Earthquake ◽  
Building Damage ◽  
Damage Classification ◽  
The 2011 Tohoku Earthquake ◽  
Benchmark Model ◽  
Siamese Network ◽  
End To End

Most mainstream research on assessing building damage using satellite imagery is based on scattered datasets and lacks unified standards and methods to quantify and compare the performance of different models. To mitigate these problems, the present study develops a novel end-to-end benchmark model, termed the pyramid pooling module semi-Siamese network (PPM-SSNet), based on a large-scale xBD satellite imagery dataset. The high precision of the proposed model is achieved by adding residual blocks with dilated convolution and squeeze-and-excitation blocks into the network. Simultaneously, the highly automated process of satellite imagery input and damage classification result output is reached by employing concurrent learned attention mechanisms through a semi-Siamese network for end-to-end input and output purposes. Our proposed method achieves F1 scores of 0.90, 0.41, 0.65, and 0.70 for the undamaged, minor-damaged, major-damaged, and destroyed building classes, respectively. From the perspective of end-to-end methods, the ablation experiments and comparative analysis confirm the effectiveness and originality of the PPM-SSNet method. Finally, the consistent prediction results of our model for data from the 2011 Tohoku Earthquake verify the high performance of our model in terms of the domain shift problem, which implies that it is effective for evaluating future disasters.

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