scholarly journals A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

2021 ◽  
Vol 13 (3) ◽  
pp. 360
Author(s):  
Wensheng Wang ◽  
Martin Gade ◽  
Kerstin Stelzer ◽  
Jörn Kohlus ◽  
Xinyu Zhao ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Wadden Sea ◽  
Relative Difference ◽  
Polarimetric Synthetic Aperture Radar ◽  
Intertidal Flats ◽  
Sediment Types ◽  
X Band ◽  
Good Potential ◽  
L Band ◽  
Sar Data

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

scholarly journals A NEW SAR CLASSIFICATION SCHEME FOR SEDIMENTS ON INTERTIDAL FLATS BASED ON MULTI-FREQUENCY POLARIMETRIC SAR IMAGERY

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 223-228 ◽  
Author(s):  
W. Wang ◽  
M. Gade
Keyword(s):  
Classification Scheme ◽  
Ground Truth ◽  
Relative Difference ◽  
Polarimetric Sar ◽  
Ground Truth Data ◽  
Intertidal Flats ◽  
X Band ◽  
Sar Data ◽  
Sar Imagery ◽  
Sandy Sediments

We present a new classification scheme for muddy and sandy sediments on exposed intertidal flats, which is based on synthetic aperture radar (SAR) data, and use ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band) fully polarimetric SAR imagery to demonstrate its effectiveness. Four test sites on the German North Sea coast were chosen, which represent typical surface compositions of different sediments, vegetation, and habitats, and of which a large amount of SAR is used for our analyses. Both Freeman-Durden and Cloude-Pottier polarimetric decomposition are utilized, and an additional descriptor called Double-Bounce Eigenvalue Relative Difference (DERD) is introduced into the feature sets instead of the original polarimetric intensity channels. The classification is conducted following Random Forest theory, and the results are verified using ground truth data from field campaigns and an existing classification based on optical imagery. In addition, the use of Kennaugh elements for classification purposes is demonstrated using both fully and dual-polarization multi-frequency and multi-temporal SAR data. Our results show that the proposed classification scheme can be applied for the discrimination of muddy and sandy sediments using L-, C-, and X-band SAR images, while SAR imagery acquired at short wavelengths (C- and X-band) can also be used to detect more detailed features such as bivalve beds on intertidal flats.

Verification of a Method for Estimating Building Damage in Extensive Tsunami Affected Areas Using L-Band SAR Data

2017 ◽  
Vol 12 (2) ◽  
pp. 251-258 ◽  
Author(s):  
Hideomi Gokon ◽  
◽  
Shunichi Koshimura ◽  
Kimiro Meguro ◽  
Keyword(s):  
Remote Sensing ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Building Damage ◽  
Pearson Correlation Coefficient ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
X Band ◽  
L Band ◽  
Sar Data

Remote sensing technology is effective for identifying the Remote sensing technology is effective for identifying the extensive damage caused by tsunami disasters. Many methods have been developed to detect building damage at the building unit scale. Of these methods, X-band Synthetic Aperture Radar (SAR) data has a high resolution and is useful to investigate the detailed conditions on the Earth’s surface, although its spatial coverage is relatively small. In contrast, L-band SAR data has a lower resolution, leading to difficulties detecting building damage, although it can cover a broad area. During disasters, it is important to understand the damage across extensive areas in a short time; therefore, it is necessary to develop a method with broad coverage with high accuracy. The primary objective of this study is to develop a method to estimate building damage in tsunami affected areas using L-band SAR (ALOS/PALSAR) data. We developed our method by extending a previously proposed method for X-band SAR (TerraSAR-X) data. This study focused on Sendai City and Watari town in Miyagi Prefecture, where many houses were washed away during the 2011 Tohoku earthquake and tsunami. We verified that the function we developed produced good performance in estimating the number of washed-away buildings, corresponding with ground truth data with a Pearson correlation coefficient of 0.97. Verification was conducted in another study area, which yielded a Pearson correlation coefficient of 0.87.

Faraday rotation effects on l-band spaceborne sar data

2003 ◽  
Vol 41 (12) ◽  
pp. 2735-2744 ◽  
Author(s):  
P.A. Wright ◽  
S. Quegan ◽  
N.S. Wheadon ◽  
C.D. Hall
Keyword(s):  
Faraday Rotation ◽  
L Band ◽  
Sar Data

Discrimination of agricultural crops in a tropical semi-arid region of Brazil based on L-band polarimetric airborne SAR data

2009 ◽  
Vol 64 (5) ◽  
pp. 458-463 ◽  
Author(s):  
Wagner F. Silva ◽  
Bernardo F.T. Rudorff ◽  
Antonio R. Formaggio ◽  
Waldir R. Paradella ◽  
José C. Mura
Keyword(s):  
Arid Region ◽  
Agricultural Crops ◽  
L Band ◽  
Sar Data ◽  
Airborne Sar ◽  
Semi Arid Region ◽  
Semi Arid
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