Parameterizing support vector machines for land cover classification
X Yang - Photogrammetric Engineering & Remote Sensing, 2011 - ingentaconnect.com
The support vector machine is a group of relatively novel statistical learning algorithms that
have not been extensively exploited in the remote sensing community. In previous studies …
have not been extensively exploited in the remote sensing community. In previous studies …
Support vector machines for land cover mapping from remote sensor imagery
D Shi, X Yang - Monitoring and Modeling of Global Changes: A …, 2015 - Springer
Land cover mapping is an important activity leading to the generation of various thematic
products essential for numerous environmental monitoring and resources management …
products essential for numerous environmental monitoring and resources management …
Ensemble of support vector machines for land cover classification
M Pal - International journal of remote sensing, 2008 - Taylor & Francis
This letter presents the results of two different ensemble approaches to increase the
accuracy of land cover classification using support vector machines. Finite ensemble …
accuracy of land cover classification using support vector machines. Finite ensemble …
Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points
Y Shao, RS Lunetta - ISPRS Journal of Photogrammetry and Remote …, 2012 - Elsevier
Support vector machine (SVM) was applied for land-cover characterization using MODIS
time-series data. Classification performance was examined with respect to training sample …
time-series data. Classification performance was examined with respect to training sample …
Classification of images using support vector machines
G Anthony, H Greg, M Tshilidzi - arXiv preprint arXiv:0709.3967, 2007 - arxiv.org
Support Vector Machines (SVMs) are a relatively new supervised classification technique to
the land cover mapping community. They have their roots in Statistical Learning Theory and …
the land cover mapping community. They have their roots in Statistical Learning Theory and …
An assessment of support vector machines for land cover classification
The support vector machine (SVM) is a group of theoretically superior machine learning
algorithms. It was found competitive with the best available machine learning algorithms in …
algorithms. It was found competitive with the best available machine learning algorithms in …
A kernel functions analysis for support vector machines for land cover classification
T Kavzoglu, I Colkesen - International Journal of Applied Earth Observation …, 2009 - Elsevier
Information about the Earth's surface is required in many wide-scale applications. Land
cover/use classification using remotely sensed images is one of the most common …
cover/use classification using remotely sensed images is one of the most common …
Multiclass approaches for support vector machine based land cover classification
M Pal - arXiv preprint arXiv:0802.2411, 2008 - arxiv.org
SVMs were initially developed to perform binary classification; though, applications of binary
classification are very limited. Most of the practical applications involve multiclass …
classification are very limited. Most of the practical applications involve multiclass …
Classification of images using support vector machines
A Gidudu, G Hulley, M Tshilidzi - 2007 - nru.uncst.go.ug
Support Vector Machines (SVMs) are a relatively new supervised classification technique to
the land cover mapping community. They have their roots in Statistical Learning Theory and …
the land cover mapping community. They have their roots in Statistical Learning Theory and …
[HTML][HTML] Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification
CA Ramezan - Remote Sensing, 2022 - mdpi.com
Remote sensing analyses frequently use feature selection methods to remove non-
beneficial feature variables from the input data, which often improve classification accuracy …
beneficial feature variables from the input data, which often improve classification accuracy …