Capability of decomposition methods for identification of crops and other land-cover targets using hybrid polarimetric SAR data

2018 ◽  
Author(s):  
Dheeraj Kumar ◽  
Atasi De ◽  
Parul Patel
Keyword(s):  
Land Cover ◽  
Decomposition Methods ◽  
Polarimetric Sar ◽  
Sar Data

Polarimetric SAR Data in Land Cover Mapping in Boreal Zone

2010 ◽  
Vol 48 (10) ◽  
pp. 3652-3662 ◽  
Author(s):  
Anne Lonnqvist ◽  
Yrjö Rauste ◽  
Matthieu Molinier ◽  
Tuomas Hame
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Boreal Zone ◽  
Polarimetric Sar ◽  
Sar Data

Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

2017 ◽  
Vol 38 (23) ◽  
pp. 7138-7160 ◽  
Author(s):  
Iman Khosravi ◽  
Abdolreza Safari ◽  
Saeid Homayouni ◽  
Heather McNairn
Keyword(s):  
Land Cover ◽  
Decision Tree ◽  
Land Cover Mapping ◽  
Polarimetric Sar ◽  
Sar Data

Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data

2017 ◽  
Vol 38 (11) ◽  
pp. 3274-3293 ◽  
Author(s):  
Bambang H. Trisasongko ◽  
Dyah R. Panuju ◽  
David J. Paull ◽  
Xiuping Jia ◽  
Amy L. Griffin
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Rural Land ◽  
Polarimetric Sar ◽  
Sar Data

scholarly journals Information Extraction from Satellite-Based Polarimetric SAR Data Using Simulated Annealing and SIRT Methods and GPU Processing

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 72
Author(s):  
Stanisława Porzycka-Strzelczyk ◽  
Jacek Strzelczyk ◽  
Kamil Szostek ◽  
Maciej Dwornik ◽  
Andrzej Leśniak ◽  
...  
Keyword(s):  
Decomposition Method ◽  
Decomposition Methods ◽  
Time Frame ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Computational Tools ◽  
Gpu Processing ◽  
Sar Data ◽  
Radar Wave ◽  
Single Pixel

The main goal of this research was to propose a new method of polarimetric SAR data decomposition that will extract additional polarimetric information from the Synthetic Aperture Radar (SAR) images compared to other existing decomposition methods. Most of the current decomposition methods are based on scattering, covariance or coherence matrices describing the radar wave-scattering phenomenon represented in a single pixel of an SAR image. A lot of different decomposition methods have been proposed up to now, but the problem is still open since it has no unique solution. In this research, a new polarimetric decomposition method is proposed that is based on polarimetric signature matrices. Such matrices may be used to reveal hidden information about the image target. Since polarimetric signatures (size 18 × 9) are much larger than scattering (size 2 × 2), covariance (size 3 × 3 or 4 × 4) or coherence (size 3 × 3 or 4 × 4) matrices, it was essential to use appropriate computational tools to calculate the results of the proposed decomposition method within an acceptable time frame. In order to estimate the effectiveness of the presented method, the obtained results were compared with the outcomes of another method of decomposition (Arii decomposition). The conducted research showed that the proposed solution, compared with Arii decomposition, does not overestimate the volume-scattering component in built-up areas and clearly separates objects within the mixed-up areas, where both building, vegetation and surfaces occur.

Classification of Polarimetric SAR Data Based on Object-Based Multiple Classifiers for Urban Land-Cover

2016 ◽  
Vol 44 (6) ◽  
pp. 855-863 ◽  
Author(s):  
Masoud Habibi ◽  
Mahmod Reza Sahebi ◽  
Yasser Maghsoudi ◽  
Shaheen Ghayourmanesh
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Polarimetric Sar ◽  
Multiple Classifiers ◽  
Urban Land Cover ◽  
Object Based ◽  
Sar Data

scholarly journals Three Strategies for Fusion of Land Cover Classification Results of Polarimetric SAR Data

10.5772/6583 ◽  
2009 ◽  
Author(s):  
Nada Milisavljevi ◽  
Isabelle Bloch ◽  
Vito Alberga ◽  
Giuseppe Satalino
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Polarimetric Sar ◽  
Sar Data
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