scholarly journals Two New Polarimetric Feature Parameters for the Recognition of the Different Kinds of Buildings in Earthquake-Stricken Areas Based on Entropy and Eigenvalues of PolSAR Decomposition

2018 ◽  
Vol 10 (10) ◽  
pp. 1613 ◽  
Author(s):  
Wei Zhai ◽  
Chunlin Huang ◽  
Wansheng Pei
Keyword(s):  
Emergency Response ◽  
Urban Areas ◽  
Supervised Classification ◽  
Polarimetric Synthetic Aperture Radar ◽  
Feature Parameters ◽  
Flight Direction ◽  
Building Information ◽  
Earthquake Damage Detection ◽  
Collapsed Buildings ◽  
Collapsed Building

Rapidly and accurately obtaining collapsed building information for earthquake-stricken areas can help to effectively guide the implementation of the emergency response and can reduce disaster losses and casualties. This work is focused on rapid building earthquake damage detection in urban areas using a single post-earthquake polarimetric synthetic aperture radar (PolSAR) image. In an earthquake-stricken area, the buildings include both damaged buildings and undamaged buildings. The undamaged buildings are made up of both parallel buildings, whose array direction is parallel to the flight direction, and oriented buildings, whose array direction is not parallel to the flight direction. The damaged buildings after an earthquake are made up of completely collapsed buildings and residual damaged parallel walls and oriented walls of the damaged buildings. Therefore, we divide the buildings in earthquake-stricken areas into five kinds: intact parallel buildings, damaged parallel walls, collapsed buildings, intact oriented buildings, and damaged oriented walls. The two new polarimetric feature parameters of λ_H and H_λ are proposed to recognize the five kinds of buildings, and the Wishart supervised classification method is employed to further improve the extraction accuracy of the damaged buildings and undamaged buildings.

scholarly journals A STATISTICAL TEXTURE FEATURE FOR BUILDING COLLAPSE INFORMATION EXTRACTION OF SAR IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 871-876
Author(s):  
L. Li ◽  
H. Yang ◽  
Q. Chen ◽  
X. Liu
Keyword(s):  
Information Extraction ◽  
Texture Feature ◽  
Statistical Distribution ◽  
Sar Images ◽  
Yushu Earthquake ◽  
Building Collapse ◽  
Building Information ◽  
Sar Data ◽  
Collapsed Buildings ◽  
Collapsed Building

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G<sup>0</sup> distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

scholarly journals Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1607
Author(s):  
Guannan Li ◽  
Ying Li ◽  
Yongchao Hou ◽  
Xiang Wang ◽  
Lin Wang
Keyword(s):  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Data ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Oil Slick ◽  
Feature Parameters ◽  
Aperture Radar

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

2020 ◽  
Vol 36 (1) ◽  
pp. 209-231
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Fumio Yamazaki ◽  
Wen Liu ◽  
Shunichi Koshimura
Keyword(s):  
Urban Areas ◽  
Road Networks ◽  
Decision Makers ◽  
Aerial Photos ◽  
Geometrical Properties ◽  
Frame Buildings ◽  
Before And After ◽  
Wood Frame ◽  
Collapsed Buildings ◽  
The Relationship

Debris scattering is one of the main causes of road/street blockage after earthquakes in dense urban areas. Therefore, the evaluation of debris scattering is crucial for decision makers and for producing an effective emergency response. In this vein, this article presents the following: (1) statistical data concerning the debris extent of collapsed buildings caused by the 2016 Mw 7.0 Kumamoto earthquake in Japan; (2) an investigation of the factors influencing the extent of debris; (3) probability functions for the debris extent; and (4) applications in the evaluation of road networks. To accomplish these tasks, LiDAR data and aerial photos acquired before and after the mainshock (16 April 2016) were used. This valuable dataset gives us the opportunity to accurately quantify the relationship between the debris extent and the geometrical properties of buildings.

Statistical Analysis of Japan Wood Frame Building Earthquake Debris Extent and Its Use in Road Networks in Japan

2019 ◽  
pp. 875529301987818
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Fumio Yamazaki ◽  
Wen Liu ◽  
Shunichi Koshimura
Keyword(s):  
Urban Areas ◽  
Road Networks ◽  
Decision Makers ◽  
Aerial Photos ◽  
Geometrical Properties ◽  
Frame Building ◽  
Before And After ◽  
Wood Frame ◽  
Collapsed Buildings ◽  
The Relationship

Debris scattering is one of the main causes of road/street blockage after earthquakes in dense urban areas. Therefore, the evaluation of debris scattering is crucial for decision-makers and for producing an effective emergency response. In this vein, this paper presents the following: (1) Statistical data concerning the debris extent of collapsed buildings caused by the 2016 Mw 7.0 Kumamoto earthquake in Japan; (2) An investigation of the factors influencing the extent of debris; (3) Probability functions for debris extent; and (4) Applications in the evaluation of road networks. To accomplish these tasks, LiDAR data and aerial photos acquired before and after the mainshock (April 16, 2016) were used. This valuable dataset gives us the opportunity to accurately quantify the relationship between the debris extent and the geometrical properties of buildings.

scholarly journals Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt

2020 ◽  
Vol 12 (20) ◽  
pp. 3307
Author(s):  
Bahaa Mohamadi ◽  
Timo Balz ◽  
Ali Younes
Keyword(s):  
Time Series ◽  
Hot Spot ◽  
Vertical Surface ◽  
Spatiotemporal Patterns ◽  
Cold Spot ◽  
Vertical Deformation ◽  
Building Collapse ◽  
Collapsed Buildings ◽  
The City ◽  
Collapsed Building

Buildings are vulnerable to collapse incidents. We adopt a workflow to detect unusual vertical surface motions before building collapses based on PS-InSAR time series analysis and spatiotemporal data mining techniques. Sentinel-1 ascending and descending data are integrated to decompose vertical deformation in the city of Alexandria, Egypt. Collapsed building data were collected from official sources, and overlayed on PS-InSAR vertical deformation results. Time series deformation residuals are used to create a space–time cube in the ArcGIS software environment and analyzed by emerging hot spot analysis to extract spatiotemporal patterns for vertical deformation around collapsed buildings. Our results show two spatiotemporal patterns of new cold spot or new hot spot before the incidents in 66 out of 68 collapsed buildings between May 2015 and December 2018. The method was validated in detail on four collapsed buildings between January and May 2019, proving the applicability of this workflow to create a temporal vulnerability map for building collapse monitoring. This study is a step forward to create a PS-InSAR based model for building collapse prediction in the city.

scholarly journals ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION

2016 ◽  
Vol III-7 ◽  
pp. 219-226
Author(s):  
J. Susaki
Keyword(s):  
Normal Distribution ◽  
Urban Areas ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Density ◽  
Concrete Blocks ◽  
Image Pixels

In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index <i>T</i><sub><i>v</i>+<i>c</i></sub> obtained by normalizing the sum of volume and helix scatterings <i>P</i><sub><i>v</i>+<i>c</i></sub>. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why <i>T</i><sub><i>v</i>+<i>c</i></sub> is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of <i>P</i><sub><i>v</i>+<i>c</i></sub> are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that <i>T</i><sub><i>v</i>+<i>c</i></sub> works most effectively because of its similarity to the normal distribution.

scholarly journals Assessing Rainwater Harvesting Potential in Urban Areas: A Building Information Modelling (BIM) Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 12583
Author(s):  
Ahsen Maqsoom ◽  
Bilal Aslam ◽  
Sharjeel Ismail ◽  
Muhammad Jamaluddin Thaheem ◽  
Fahim Ullah ◽  
...  
Keyword(s):  
Water Scarcity ◽  
Urban Areas ◽  
Rainwater Harvesting ◽  
Residential Buildings ◽  
Assessment System ◽  
Information Modeling ◽  
Small Enterprise ◽  
Human Needs ◽  
Building Information ◽  
Roofing Material

Water scarcity has become a major problem for many countries, resulting in declining water supply and creating a need to find alternative solutions. One potential solution is rainwater harvesting (RwH), which allows rainwater to be stored for human needs. This study develops an RwH assessment system through building information modeling (BIM). For this purpose, a hydrological study of Cfa-type climate cities is conducted with the example of Islamabad, Pakistan. The monthly rainfall data of three sites were assessed to determine the volume of the accumulated rainwater and its potential to meet human needs. The average number of people living in a house is taken as the household number. Household number or of the number of employees working at a small enterprise, roofing material, and rooftop area are used as the key parameters for pertinent assessment in the BIM. The data simulated by BIM highlight the RwH potential using five people per house as the occupancy and a 90 m2 rooftop area for residential buildings or small enterprises as parameters. The results show that the selected sites can collect as much as 8,190 L/yr of rainwater (48 L/person/day) to 103,300 L/yr of rainwater (56 L/person/day). This much water is enough to fulfill the daily demands of up to five people. Therefore, it is established that the study area has an RwH potential that is able to meet the expected demands. This study presents a baseline approach for RwH to address water scarcity issues for residential buildings and factories of the future.

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