Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake

2017 ◽  
Vol 12 (sp) ◽  
pp. 646-655 ◽  
Author(s):  
Yanbing Bai ◽  
Bruno Adriano ◽  
Erick Mas ◽  
Shunichi Koshimura ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Building Damage ◽  
2016 Kumamoto Earthquake ◽  
Sar Images ◽  
Kumamoto Earthquake ◽  
Multi Temporal ◽  
Sar Data ◽  
Sar Imagery ◽  
Damage Mapping

Synthetic Aperture Radar (SAR) remote sensing is a useful tool for mapping earthquake-induced building damage. A series of operational methodologies based on SAR data using either multi-temporal or only post-event SAR images have been developed and used to serve disaster activities. This presents a critical problem: which method is more likely to obtain reliable results and should be adopted for disaster response when both pre- and post-event SAR data are available? To explore this question, this study takes the 2016 Kumamoto earthquake as a case study. ALOS-2/PALSAR-2 SAR images were employed with a machine learning framework to quantitatively compare the performance of building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images. The results show that an overall accuracy of 64.5% was achieved when only post-event SAR images were used, which is 2.3% higher than the overall accuracy when multi-temporal SAR images were used. The estimated building damage ratio for the former and the latter are 29.7% and 31.1%, respectively, which are both close to the building damage ratio obtained from an optical image.

Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco

2021 ◽  
Author(s):  
Brahim Benzougagh ◽  
Pierre-Louis Frison ◽  
Sarita Gajbhiye Meshram ◽  
Larbi Boudad ◽  
Abdallah Dridri ◽  
...  
Keyword(s):  
Flood Mapping ◽  
Sar Images ◽  
Multi Temporal

scholarly journals Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

2019 ◽  
Vol 11 (13) ◽  
pp. 1582 ◽  
Author(s):  
Mahdianpari ◽  
Mohammadimanesh ◽  
McNairn ◽  
Davidson ◽  
Rezaee ◽  
...  
Keyword(s):  
Variable Importance ◽  
Sar Images ◽  
Object Based ◽  
Multi Temporal ◽  
Importance Analysis ◽  
Crop Mapping ◽  
Crop Classification ◽  
Sar Data ◽  
Highly Correlated ◽  
Marginal Improvement

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

Investigation of Damages in Immediate Vicinity of Co-Seismic Faults During the 2016 Kumamoto Earthquake

2017 ◽  
Vol 12 (5) ◽  
pp. 899-915 ◽  
Author(s):  
Shohei Naito ◽  
Ken Xiansheng Hao ◽  
Shigeki Senna ◽  
Takuma Saeki ◽  
Hiromitsu Nakamura ◽  
...  
Keyword(s):  
Fault Zone ◽  
Active Fault ◽  
Strong Motion ◽  
Site Amplification ◽  
Building Damage ◽  
2016 Kumamoto Earthquake ◽  
Severe Damage ◽  
Kumamoto Earthquake ◽  
The 2016 Kumamoto Earthquake ◽  
The Relationship

In the 2016 Kumamoto earthquake, the Futagawa fault zone and the Hinagu fault zone were active in some sections, causing severe damage in neighboring areas along the faults. We conducted a detailed investigation of the surface earthquake fault, building damage, and site amplification of shallow ground within about 1 km of the neighboring areas of the fault. The focus was mainly on Kawayou district, Minamiaso village and Miyazono district, Mashiki town, and locations that suffered particularly severe building damage. We explored the relationship between local strong motion and building damage caused in areas that were in the immediate vicinity of the active fault.

scholarly journals Similarity measures of full polarimetric SAR images fusion for improved SAR image matching

2015 ◽  
Vol XL-7/W4 ◽  
pp. 13-16 ◽  
Author(s):  
H. Ding
Keyword(s):  
Similarity Measure ◽  
Image Matching ◽  
Stereo Matching ◽  
Similarity Measures ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Sar Data ◽  
Sar Imagery ◽  
Airborne Sar

China’s first airborne SAR mapping system (CASMSAR) developed by Chinese Academy of Surveying and Mapping can acquire high-resolution and full polarimetric (HH, HV, VH and VV) Synthetic aperture radar (SAR) data. It has the ability to acquire X-band full polarimetric SAR data at a resolution of 0.5m. However, the existence of speckles which is inherent in SAR imagery affects visual interpretation and image processing badly, and challenges the assumption that conjugate points appear similar to each other in matching processing. In addition, researches show that speckles are multiplicative speckles, and most similarity measures of SAR image matching are sensitive to them. Thus, matching outcomes of SAR images acquired by most similarity measures are not reliable and with bad accuracy. Meanwhile, every polarimetric SAR image has different backscattering information of objects from each other and four polarimetric SAR data contain most basic and a large amount of redundancy information to improve matching. Therefore, we introduced logarithmically transformation and a stereo matching similarity measure into airborne full polarimetric SAR imagery. Firstly, in order to transform the multiplicative speckles into additivity ones and weaken speckles' influence on similarity measure, logarithmically transformation have to be taken to all images. Secondly, to prevent performance degradation of similarity measure caused by speckles, measure must be free or insensitive of additivity speckles. Thus, we introduced a stereo matching similarity measure, called Normalized Cross-Correlation (NCC), into full polarimetric SAR image matching. Thirdly, to take advantage of multi-polarimetric data and preserve the best similarity measure value, four measure values calculated between left and right single polarimetric SAR images are fused as final measure value for matching. The method was tested for matching under CASMSAR data. The results showed that the method delivered an effective performance on experimental imagery and can be used for airborne SAR matching applications.

Sign in / Sign up

Export Citation Format

Share Document