scholarly journals Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery

Water ◽  
2013 ◽  
Vol 5 (3) ◽  
pp. 1036-1051 ◽  
Author(s):  
Andreas Schmitt ◽  
Brian Brisco
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Polarimetric Sar ◽  
Wetland Monitoring ◽  
Sar Imagery

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%.

scholarly journals AN UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND KI FROM MULTI-TEMPORAL AND FULL POLARIMETRIC SAR IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 611-615
Author(s):  
J. Q. Zhao ◽  
J. Yang ◽  
P. X. Li ◽  
M. Y. Liu ◽  
Y. M. Shi
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Speckle Noise ◽  
Natural Phenomena ◽  
Threshold Selection ◽  
Test Statistic ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Multi Temporal ◽  
Selection Of

Accurate and timely change detection of Earth’s surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.

Combined analysis of polarimetric SAR data and optical imagery for rapid landslide mapping in vegetated areas

2020 ◽  
Author(s):  
Simon Plank ◽  
Sandro Martinis
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Optical Data ◽  
Polarimetric Sar ◽  
Rapid Mapping ◽  
Landslide Event ◽  
Landslide Mapping ◽  
Optical Imagery ◽  
Sar Data ◽  
Sar Imagery

<p>Rapid mapping of the extent of the affected area as well as type and grade of damage after a landslide event is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Change detection between pre- and post-event very high resolution (VHR) optical imagery is the state-of-the-art in operational rapid mapping of landslides. However, the suitability of optical data relies on clear sky conditions, which is not often the case after landslides events, as heavy rain is one of the most frequent triggers of landslides. In contrast to this, the acquisition of synthetic aperture radar (SAR) imagery is independent of atmospheric conditions. SAR data-based landslide detection approaches reported in the literature use change detection techniques, requiring VHR SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides in vegetated areas, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.</p>

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