scholarly journals New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

2017 ◽  
Vol 122 (4) ◽  
pp. 767-795 ◽  
Author(s):  
Kazuhito Ichii ◽  
Masahito Ueyama ◽  
Masayuki Kondo ◽  
Nobuko Saigusa ◽  
Joon Kim ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Support Vector Regression ◽  
Eddy Covariance ◽  
Remote Sensing Data ◽  
Data Driven ◽  
Support Vector ◽  
Co2 Fluxes ◽  
Sensing Data ◽  
Eddy Covariance Measurements

scholarly journals Estimating biophysical parameters of rice with remote sensing data using support vector machines

2011 ◽  
Vol 54 (3) ◽  
pp. 272-281 ◽  
Author(s):  
XiaoHua Yang ◽  
JingFeng Huang ◽  
YaoPing Wu ◽  
JianWen Wang ◽  
Pei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Biophysical Parameters ◽  
Sensing Data ◽  
Vector Machines

scholarly journals A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna L Buczak ◽  
Phillip T Koshute ◽  
Steven M Babin ◽  
Brian H Feighner ◽  
Sheryl H Lewis
Keyword(s):  
Remote Sensing ◽  
Prediction Method ◽  
Remote Sensing Data ◽  
Data Driven ◽  
Sensing Data ◽  
Dengue Outbreaks

scholarly journals Estimation of soil types by non linear analysis of remote sensing data

2008 ◽  
Vol 15 (1) ◽  
pp. 115-126 ◽  
Author(s):  
C. Hahn ◽  
R. Gloaguen
Keyword(s):  
Remote Sensing ◽  
Soil Type ◽  
Remote Sensing Data ◽  
Soil Types ◽  
Support Vector ◽  
Sensing Data ◽  
Vector Machines ◽  
Non Linear ◽  
Input Variables ◽  
Type Classification

Abstract. The knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation.

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.

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