Building damage assessment from PolSAR data using texture parameters of statistical model

被引:23
|
作者
Li, Linlin [1 ]
Liu, Xiuguo [1 ]
Chen, Qihao [1 ]
Yang, Shuai [1 ]
机构
[1] China Univ Geosci Wuhan, Fac Infonnat Engn, Wuhan 430074, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Polarimetric synthetic aperture radar (PolSAR); Disaster assessment; Building damage assessment; Texture; Statistical model; SAR; EARTHQUAKE; IMAGERY; AREA;
D O I
10.1016/j.cageo.2018.01.018
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Accurate building damage assessment is essential in providing decision support for disaster relief and reconstruction. Polarimetric synthetic aperture radar (PolSAR) has become one of the most effective means of building damage assessment, due to its all-day/all-weather ability and richer backscatter information of targets. However, intact buildings that are not parallel to the SAR flight pass (termed oriented buildings) and collapsed buildings share similar scattering mechanisms, both of which are dominated by volume scattering. This characteristic always leads to misjudgments between assessments of collapsed buildings and oriented buildings from PolSAR data. Because the collapsed buildings and the intact buildings (whether oriented or parallel buildings) have different textures, a novel building damage assessment method is proposed in this study to address this problem by introducing texture parameters of statistical models. First, the logarithms of the estimated texture parameters of different statistical models are taken as a new texture feature to describe the collapse of the buildings. Second, the collapsed buildings and intact buildings are distinguished using an appropriate threshold. Then, the building blocks are classified into three levels based on the building block collapse rate. Moreover, this paper also discusses the capability for performing damage assessment using texture parameters from different statistical models or using different estimators. The RADARSAT-2 and ALOS-1 PolSAR images are used to present and analyze the performance of the proposed method. The results show that using the texture parameters avoids the problem of confusing collapsed and oriented buildings and improves the assessment accuracy. The results assessed by using the K/G(0) distribution texture parameters estimated based on the second moment obtain the highest extraction accuracies. For the RADARSAT-2 and ALOS-1 data, the overall accuracy (OA) for these three types of buildings is 73.39% and 68.45%, respectively.
引用
收藏
页码:115 / 126
页数:12
相关论文
共 50 条
  • [31] Classification of cloudiness from MODIS satellite data using regional statistical models for image texture and physical parameters of cloudiness during periods with snow cover
    Astafurov, V. G.
    Skorokhodov, A. V.
    Musienko, O. P.
    Kuriyanovich, K. V.
    24TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS: ATMOSPHERIC PHYSICS, 2018, 10833
  • [32] Seismic Building Damage Prediction From GIS-Based Building Data Using Artificial Intelligence System
    Hansapinyo, Chayanon
    Latcharote, Panon
    Limkatanyu, Suchart
    FRONTIERS IN BUILT ENVIRONMENT, 2020, 6
  • [33] Rapid building damage assessment using EROS B data: the case study of L'Aquila earthquake
    Baiocchi, Valerio
    Dominici, Donatella
    Giannone, Francesca
    Zucconi, Maria
    ITALIAN JOURNAL OF REMOTE SENSING-RIVISTA ITALIANA DI TELERILEVAMENTO, 2012, 44 (01): : 153 - 165
  • [34] Urban Area Man-Made Target Detection for PolSAR Data Based on a Nonzero-Mean Statistical Model
    Wu, W.
    Guo, H.
    Li, X.
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (10) : 1782 - 1786
  • [35] Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake
    Sun, Xiaolin
    Chen, Xi
    Yang, Liao
    Wang, Weisheng
    Zhou, Xixuan
    Wang, Lili
    Yao, Yuan
    REMOTE SENSING, 2022, 14 (13)
  • [36] Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake
    Wei Zhai
    Chunlin Huang
    Earth, Planets and Space, 68
  • [37] Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake
    Zhai, Wei
    Huang, Chunlin
    EARTH PLANETS AND SPACE, 2016, 68
  • [38] Building detection from high-resolution PolSAR data at the rectangle level by combining region and edge information
    Wang, Yinghua
    Tupin, Florence
    Han, Chongzhao
    PATTERN RECOGNITION LETTERS, 2010, 31 (10) : 1077 - 1088
  • [39] Building Damage Assessment Using Feature Concatenated Siamese Neural Network
    Ramadhan, Mgs M. Luthfi
    Jati, Grafika
    Jatmiko, Wisnu
    IEEE ACCESS, 2024, 12 : 19100 - 19116
  • [40] BUILDING DAMAGE ASSESSMENT OVER UKRAINE USING SAR TIME SERIES
    Sica, Francescopaolo
    Leffler, Tim
    Schmitt, Michael
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 2656 - 2658