Environmental impact classification of urban land cover from satellite remote sensing data

2005 ◽  
Author(s):  
L. F. V. Zoran ◽  
C. Golovanov ◽  
M. A. Zoran
Keyword(s):  
Remote Sensing ◽  
Environmental Impact ◽  
Land Cover ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Sensing Data ◽  
Urban Land Cover

scholarly journals Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery

2021 ◽  
Vol 10 (8) ◽  
pp. 533
Author(s):  
Bin Hu ◽  
Yongyang Xu ◽  
Xiao Huang ◽  
Qimin Cheng ◽  
Qing Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Sensing Data ◽  
Urban Land Cover ◽  
Sar Data

Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

2017 ◽  
Vol 14 (5) ◽  
pp. 778-782 ◽  
Author(s):  
Nataliia Kussul ◽  
Mykola Lavreniuk ◽  
Sergii Skakun ◽  
Andrii Shelestov
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Crop Types
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