چکیده:
The objective of this research was to determine the best model and compare performances in terms of producing land
use maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance of
mean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral angle
mapper (SAM), and support vector machine (SVM) were considered in three areas of Iran's dry climate. The selected
study areas for dry climates were Shahreza, Taft and Zarand in Isfahan, Yazd, and Kerman Provinces, respectively. Three
Landsat ETM+ images and topographical maps of 1:25,000-scale were used in the present study. In addition, training
samples for each land use were constructed using GPS and extensive field surveys. The training sites were divided into
two categories; one category was used for image classification and the other for classification accuracy assessment.
Results show that for the dry climate areas, Maximum Likelihood and Support Vector Machine algorithms with averages
of 0.9409 and 0.9315 Kappa coefficients are the best algorithms for land use mapping. The ANOVA test was performed on
Kappa coefficients, and the result shows that there are significant differences at the 1% level, between the different
algorithms for the dry climate zones. These results can be used for land use planning, as well as environmental and natural
resources purposes in study areas.
خلاصه ماشینی:
"Desert 20-1 (2015) 1-10 Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran S.
As a result, different algorithms such as the minimum distance of mean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral angle mapper (SAM), and support vector machine (SVM) were considered in three areas of Iran's dry climate.
Several different classification algorithms are used to produce land use maps from remote sensing and satellite images namely Maximum Likelihood, Neural network and Support Vector Machines (Tso and Mather, 2001; Franklin and Wulder, 2002; Frery et al.
Artificial Neural Network is one of the nonparametric algorithms used for image classification that does not need to assume a normal distribution of data (Kavzoglu and Mather, 2003; Qiu and Jensen, 2004; Foody, 2004; Lu and Weng, 2007; Dixon and Candade, 2008).
Researches are currently ongoing, regarding the methods of satellite image classification and the Support Vector Machine (SVM) is a recently introduced algorithm for satellite image classification to map land use (Huang et al.
, 2006; Al-Ahmadi and Hames, 2009; Rajesh and Yuji, 2009; Perumal and Bhaskaran, 2010; Brian et al.
3. Results and discussion To produce land use maps for each case study, different algorithms such as Support Vector Machine, Maximum Likelihood, Neural network, Minimum Distance, Mahalanobis Distance and Spectral Angle Mapper were used.
Multispectral land use classification using neural networks and support vector machines: one or the other, or both, International Journal of Remote Sensing 29; 1185–1206."