Accurate Coherent Change Detection Method Based on Pauli Decomposition for Fully Polarimetric SAR Imagery

2015 ◽  
Vol E98.B (7) ◽  
pp. 1390-1395
Author(s):  
Ryo OYAMA ◽  
Shouhei KIDERA ◽  
Tetsuo KIRIMOTO
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Polarimetric Sar ◽  
Coherent Change Detection ◽  
Sar Imagery

scholarly journals POST-DISASTER DAMAGE ASSESSMENT THROUGH COHERENT CHANGE DETECTION ON SAR IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 431-436 ◽  
Author(s):  
L. Guida ◽  
P. Boccardo ◽  
I. Donevski ◽  
L. Lo Schiavo ◽  
M. E. Molinari ◽  
...  
Keyword(s):  
Change Detection ◽  
Damage Assessment ◽  
Central Italy ◽  
Weather Conditions ◽  
Earthquake Scenario ◽  
Central Italy Earthquake ◽  
Novel Approach ◽  
Coherent Change Detection ◽  
Sar Imagery ◽  
Bad Weather

Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR) imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD) algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS). A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

A New Automatic Ship Detection Method Using $L$-Band Polarimetric SAR Imagery

2014 ◽  
Vol 7 (4) ◽  
pp. 1383-1393 ◽  
Author(s):  
Jujie Wei ◽  
Pingxiang Li ◽  
Jie Yang ◽  
Jixian Zhang ◽  
Fengkai Lang
Keyword(s):  
Detection Method ◽  
Polarimetric Sar ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Wishart-Based Adaptive Temporal Filtering of Polarimetric SAR Imagery

2020 ◽  
Vol 12 (15) ◽  
pp. 2454 ◽  
Author(s):  
Morton J. Canty ◽  
Allan A. Nielsen ◽  
Henning Skriver ◽  
Knut Conradsen
Keyword(s):  
Change Detection ◽  
Detection Algorithm ◽  
Google Earth ◽  
Speckle Reduction ◽  
Intensity Data ◽  
Dual Polarization ◽  
Polarimetric Sar ◽  
Temporal Filtering ◽  
Google Earth Engine ◽  
Sar Imagery

Temporal filtering for speckle reduction of polarimetric SARimages is described. The method is based on a sequential complex Wishart-based change detection algorithm which is applied to polarized SAR imagery, including the dual-polarization intensity data archived on the Google Earth Engine (GEE). Software for convenient application and analysis is presented. Results compare favorably with, and improve upon, standard spatial and temporal filters for speckle reduction.

scholarly journals Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data

2020 ◽  
Vol 12 (1) ◽  
pp. 137 ◽  
Author(s):  
Sang-Eun Park ◽  
Yoon Taek Jung
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Detection Method ◽  
Building Damage ◽  
Polarimetric Sar ◽  
Ground Shaking ◽  
The 2016 Kumamoto Earthquake ◽  
Radar Scattering ◽  
The Matrix ◽  
Damage Indicators

Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%.

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