scholarly journals Modification of Freeman-Durden Decomposition to Eliminate the Negative Power Problem

2020 ◽  
Author(s):  
Asset Akhmadiya ◽  
Khuralay Moldamurat ◽  
Nabi Nabiyev
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
New Method ◽  
Matrix Elements ◽  
Negative Power ◽  
Radar Remote Sensing ◽  
Qinghai Province ◽  
Sensing Data ◽  
Comparison Results ◽  
Power Problem

A new method in which completely eliminated the negative value of the scattering power is proposed in this paper. Primarily, here are presented the Freeman-Durden decomposition of the scattering powers, which are computed by using the coherency matrix elements with rotation and without rotation. Secondly, this paper investigates the reasons of the occurrence of negative values of the scattering powers. Then using the modification like in algorithm proposed by Yamaguchi for G4U, it was applied for the algorithm of Freeman-Durden decomposition to eliminate negative values. In this research, Radarsat-2 radar remote sensing data were used, which are acquired for study area Yushu County, Qinghai province, China. At the end, the comparison results of Yamaguchi G4U and a modified Freeman-Durden decompositions were presented.

A new method of sample selections for optimizing phenology model based remote sensing data

2015 ◽  
Vol 39 (3) ◽  
pp. 264-274
Author(s):  
MA Yong-Gang ◽  
◽  
ZHANG Chi ◽  
CHEN Xi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
New Method ◽  
Sensing Data ◽  
Model Based ◽  
Phenology Model

scholarly journals A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

2016 ◽  
Vol 8 (1) ◽  
pp. 70 ◽  
Author(s):  
Neha Joshi ◽  
Matthias Baumann ◽  
Andrea Ehammer ◽  
Rasmus Fensholt ◽  
Kenneth Grogan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Radar Remote Sensing ◽  
Sensing Data

scholarly journals Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters

2020 ◽  
Author(s):  
Asset Akhmadiya ◽  
Nabi Nabiyev ◽  
Khuralay Moldamurat ◽  
Kanagat Dyusekeev ◽  
Sabyrzhan Atanov
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Damage Assessment ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Research Paper ◽  
Building Damage ◽  
Main Task ◽  
Radar Remote Sensing ◽  
Sensing Data

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

Improving a digital elevation model of Antarctica using radar remote sensing data and GIS techniques1

1999 ◽  
Vol 23 (3) ◽  
pp. 185-200 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Hongxing Liu ◽  
Zhiyuan Zhao ◽  
Biyan Li
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Radar Remote Sensing ◽  
Sensing Data ◽  
Digital Elevation ◽  
Elevation Model
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