Staff Members - Benha University
Support Vector Machines: Optimization and validation for land cover mapping using aerial images and lidar data
34th Int. Symp. on Remote Sensing of Environment • 2011
Publication Information
Authors
JC Trinder, M Salah
Keywords
Aerial Images, Lidar, GLCM, Attributes, SVMs,
Optimization, Validation
Journal
34th Int. Symp. on Remote Sensing of Environment
Publisher
Not Available
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
This work investigates the optimization and
validation of Support Vector Machines (SVMs) for land
cover classification from multispectral aerial imagery and
lidar data. For the optimization step, a new method based
on a curve fitting technique was applied to minimize the
grid search for the Gaussian Radius Basis Function (RBF)
parameters. The validation step was based on two
experiments. In the first, four SVM kernel models
(Gaussian Radius Basis Function; Linear; Polynomial; and
Sigmoid) were tested and compared to each other. In the
second, SVMs were compared against two classifiers of
different characteristics (the Self-Organizing Map (SOM)
and the Classification Trees (CTs)) based on four study
areas with different sensor and scene characteristics. The
comparison is based on two criteria: overall classification
accuracy; and commission and omission errors per class.
The results demonstrate: the higher overall classification
accuracy; the lower range of commission and omission
errors per class of the SVMs as compared to other
classifiers
validation of Support Vector Machines (SVMs) for land
cover classification from multispectral aerial imagery and
lidar data. For the optimization step, a new method based
on a curve fitting technique was applied to minimize the
grid search for the Gaussian Radius Basis Function (RBF)
parameters. The validation step was based on two
experiments. In the first, four SVM kernel models
(Gaussian Radius Basis Function; Linear; Polynomial; and
Sigmoid) were tested and compared to each other. In the
second, SVMs were compared against two classifiers of
different characteristics (the Self-Organizing Map (SOM)
and the Classification Trees (CTs)) based on four study
areas with different sensor and scene characteristics. The
comparison is based on two criteria: overall classification
accuracy; and commission and omission errors per class.
The results demonstrate: the higher overall classification
accuracy; the lower range of commission and omission
errors per class of the SVMs as compared to other
classifiers