Optical properties of Na<sub>0.68</sub>CoO<sub>2</sub> thin film

Hsu, Miao Hsing; Liu, Hsiang Lin; Chang, Wen‐Hao; Lin, Jiunn-Yuan

doi:10.1002/pssc.200777159

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2008

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“…The RBF kernel was chosen because this kernel is able to handle nonlinear relations between class labels and attributes, and only one kernel parameter has to be defined (Hsu et al, 2007).…”

Section: Svm Settingsmentioning

confidence: 99%

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

Hahn

Gloaguen

2008

Nonlin. Processes Geophys.

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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 nonlinear, 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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“…The RBF kernel was chosen because this kernel is able to handle nonlinear relations between class labels and attributes, and only one kernel parameter has to be defined (Hsu et al, 2007).…”

Section: Svm Settingsmentioning

confidence: 99%