Identification of changes in urban land cover type using fully polarimetric SAR data

2016 ◽  
Vol 7 (7) ◽  
pp. 691-700 ◽  
Author(s):  
Lei Xie ◽  
Hong Zhang ◽  
Meng Liu ◽  
Chao Wang
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Land Cover Type ◽  
Polarimetric Sar ◽  
Cover Type ◽  
Urban Land Cover ◽  
Sar Data

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 Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0228537 ◽  
Author(s):  
Elí R. Pérez-Ruiz ◽  
Enrique R. Vivoni ◽  
Nicole P. Templeton
Keyword(s):  
Carbon Dioxide ◽  
Land Cover ◽  
Urban Land ◽  
Land Cover Type ◽  
Carbon Dioxide Fluxes ◽  
Cover Type ◽  
Urban Land Cover

Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

2022 ◽  
Vol 88 (1) ◽  
pp. 17-28
Author(s):  
Qing Ding ◽  
Zhenfeng Shao ◽  
Xiao Huang ◽  
Orhan Altan ◽  
Yewen Fan
Keyword(s):  
Feature Selection ◽  
Land Cover ◽  
Urban Land ◽  
Model Complexity ◽  
Selection Strategy ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Selection Methods ◽  
Urban Land Cover ◽  
Sar Data

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.

Urban land cover classification with high-resolution polarimetric SAR interferometric data

2010 ◽  
Vol 36 (3) ◽  
pp. 236-247 ◽  
Author(s):  
Xinwu Li ◽  
Eric Pottier ◽  
Huadong Guo ◽  
Laurent Ferro-Famil
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Polarimetric Sar ◽  
Urban Land Cover
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