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 Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data

2010 ◽  
Vol 48 (8) ◽  
pp. 3188-3197 ◽  
Author(s):  
Jordi Mũnoz-Marí ◽  
Francesca Bovolo ◽  
Luis Gómez-Chova ◽  
Lorenzo Bruzzone ◽  
Gustavo Camp-Valls
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Sensing Data ◽  
Vector Machines

Towards automatic lithological classification from remote sensing data using support vector machines

2012 ◽  
Vol 45 ◽  
pp. 229-239 ◽  
Author(s):  
Le Yu ◽  
Alok Porwal ◽  
Eun-Jung Holden ◽  
Michael C. Dentith
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Sensing Data ◽  
Vector Machines

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.

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