A kernel functions analysis for support vector machines for land cover classification

By Taskin Kavzoglu, Ismail Colkesen. International Journal of Applied Earth Observation and Geoinformation, 2009

707 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/53dd4141f85857c366766c1ac5f78c2b7392372e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Ecology

In Media Technology

Citations in the corpus

Using geographically weighted variables for image classification (2012, background): “Kavzoglu and Colkensen (2009) give an overview of the commonly used SVM kernels in remote sensing and their functionality.”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, methodology): “However, the idea behind the kernel trick is to map the feature space into a higher dimension (Euclidean or Hilbert space) to improve the separability between classes [78], [79].”
Support vector machine to map oil palm in a heterogeneous environment (2014, background): “It then operates to find a wide separation boundary between class pairs by marking each pixel as belonging to a class, based on the inputs (Foody and Mathur 2004a; Kavzoglu and Colkesen 2009).”
Support vector machine: A tool for mapping mineral prospectivity (2011, methodology): “Certain types of SVMs have been developed and applied successfully to text categorization, handwriting recognition, gene-function prediction, remote sensing classification and other studies (e.g., Joachims 1998; Huang et al.,2002; Cristianini and Scholkopf, 2002; Guo et al., 2005; Kavzoglu and Colke”
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery (2014, methodology): “To map non-linear decision boundaries into linear ones in a higher dimension, the four most frequently used types of kernel functions in SVM algorithms are linear, polynomial, radial basis function (RBF) and sigmoid kernels [26].”
Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression (2014, methodology): “Support vector machines SVMs based on the statistical learning theory has been widely used as a data mining technique to solve many complex classification and regression problems (Brenning 2005; Kavzoglu and Colkesen 2009; Ballabio and Sterlacchini 2012).”
Implementation of machine-learning classification in remote sensing: an applied review (2018, background): “There are many possible kernels (Kavzoglu and Colkesen 2009), and each kernel may have a di ﬀ erent set of required user-speci ﬁ ed parameters.”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, background): “A number of authors have found that the radial basis function outperforms the polynomial function (Kavzoglu and Colkesen 2009b; Waske et al. 2010; Huang et al. 2008; Oommen et al. 2008).”
Support vector machines in remote sensing: A review (2011, neutral): “…to define the kernel functions for each data source and level separately; their multiple classifier system improved the performance compared to a single classifier approach since the individual errors of multiple data sources at different aggregation scales were diverse and uncorrelated.”
Remote sensing for wetland classification: a comprehensive review (2018, background): “However, it should be noted that SVM needs a kernel function, defining which is time-consuming and subjective (Kavzoglu and Colkesen 2009; Qian et al. 2014).”