scholarly journals A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

2017 ◽  
Vol 9 (10) ◽  
pp. 1013 ◽  
Author(s):  
Tsitsi Bangira ◽  
Silvia Alfieri ◽  
Massimo Menenti ◽  
Adriaan van Niekerk ◽  
Zoltán Vekerdy
Keyword(s):  
Spectral Unmixing ◽  
Flood Mapping ◽  
Ensemble Estimation

scholarly journals DLR HySU—A Benchmark Dataset for Spectral Unmixing

2021 ◽  
Vol 13 (13) ◽  
pp. 2559
Author(s):  
Daniele Cerra ◽  
Miguel Pato ◽  
Kevin Alonso ◽  
Claas Köhler ◽  
Mathias Schneider ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Benchmark Dataset ◽  
Data Sets ◽  
Endmember Extraction ◽  
Imaging Spectrometer ◽  
Hyperspectral Unmixing ◽  
Processing Step ◽  
German Aerospace ◽  
Spectrometer Data ◽  
First Time

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco

2021 ◽  
Author(s):  
Brahim Benzougagh ◽  
Pierre-Louis Frison ◽  
Sarita Gajbhiye Meshram ◽  
Larbi Boudad ◽  
Abdallah Dridri ◽  
...  
Keyword(s):  
Flood Mapping ◽  
Sar Images ◽  
Multi Temporal

Ensemble estimation of polarization ellipse parameters

2007 ◽  
Vol 463 (2088) ◽  
pp. 3375-3394 ◽  
Author(s):  
A.T Walden ◽  
T Medkour
Keyword(s):  
Angular Distribution ◽  
Standard Deviation ◽  
Aspect Ratio ◽  
Plane Wave ◽  
Wave Field ◽  
Covariance Matrix ◽  
Time Instant ◽  
Azimuth Angle ◽  
Ensemble Estimation ◽  
Plane Wave Field

An ellipse describes the polarized part of a partially polarized quasi-monochromatic plane wave field. Its azimuth angle and aspect ratio are functions of the elements of the covariance matrix associated with the polarized part at a particular time instant. Given an ensemble of K independent samples at that time, the distributions of the estimators of these parameters are derived. The estimation is thus based on a sample ensemble at any time, and does not assume temporal stationarity. Additionally, the azimuth angle estimator has an angular distribution so that non-standard statistical methods are needed when deriving its mean and standard deviation.

scholarly journals Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia

2021 ◽  
Vol 13 (8) ◽  
pp. 4115
Author(s):  
Jaka Budiman ◽  
Jarbou Bahrawi ◽  
Asep Hidayatulloh ◽  
Mansour Almazroui ◽  
Mohamed Elhag
Keyword(s):  
Saudi Arabia ◽  
Flash Flood ◽  
Remote Sensing Data ◽  
Surface Elevation ◽  
Flood Mapping ◽  
Depth Information ◽  
Arid Environments ◽  
Volume Calculation ◽  
Flood Depth ◽  
Flood Volume

Actual flood mapping and quantification in an area provide valuable information for the stakeholder to prevent future losses. This study presents the actual flash flood quantification in Al-Lith Watershed, Saudi Arabia. The study is divided into two steps: first is actual flood mapping using remote sensing data, and the second is the flood volume calculation. Two Sentinel-1 images are processed to map the actual flood, i.e., image from 25 May 2018 (dry condition), and 24 November 2018 (peak flood condition). SNAP software is used for the flood mapping step. During SNAP processing, selecting the backscatter data representing the actual flood in an arid region is challenging. The dB range value from 7.23–14.22 is believed to represent the flood. In GIS software, the flood map result is converted into polygon to define the flood boundary. The flood boundary that is overlaid with Digital Elevation Map (DEM) is filled with the same elevation value. The Focal Statistics neighborhood method with three iterations is used to generate the flood surface elevation inside the flood boundary. The raster contains depth information is derived by subtraction of the flood surface elevation with DEM. Several steps are carried out to minimize the overcalculation outside the flood boundary. The flood volume can be derived by the multiplication of flood depth points with each cell size area. The flash flood volume in Al-Lith Watershed on 24 November 2018 is 155,507,439 m3. Validity checks are performed by comparing it with other studies, and the result shows that the number is reliable.

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