COMPARISON OF SUPPORT VECTOR MACHINE, NEURAL NETWORK, AND MAXIMUM LIKELIHOOD METHODS FOR THE SEPARATION OF LITHOLOGICAL UNITS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GODARZI MEHR S.; ABBASPOUR R.A.; AHADNEZHAD V.; KHAKBAZ B.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Geology Year:2012 | Volume:6 | Issue:22 Page(s): 75-92

Download Full-Text

Persian Verion

View:

2,305

Download:

Cites:

Information Journal Paper

Title

COMPARISON OF SUPPORT VECTOR MACHINE, NEURAL NETWORK, AND MAXIMUM LIKELIHOOD METHODS FOR THE SEPARATION OF LITHOLOGICAL UNITS

Author(s)

GODARZI MEHR S. | ABBASPOUR R.A. | AHADNEZHAD V. | KHAKBAZ B. | Issue Writer Certificate

Keywords

REMOTE SENSINGQ2

TM5Q2

MAXIMUM LIKELIHOODQ1

NEURAL NETWORKSQ2

SVM-CLASSIFICATIONQ2

Abstract

Since the spectrum behavior of rocks is similar, preparing of geological maps by using of multi-spectral images could be difficult. In this study, the Support Vector Machine (SVM) is used as one of the image classification methods, which has flexibility for individual conditions. The kernels of SVM are performed by MAXIMUM LIKELIHOOD (MLC) and Neural Network Classification (NNC) methods in order to produce geological maps and training samples of hand and laboratory samples, as well. The results show that the SVM method has highest precision (83.42%) in all three kernels in comparison with other two methods. Furthermore, this method can reach the 100% precision if uses only 50% of training samples, while in other methods (i.e. MLC and NNC) can not reach the 100% Precision Comparing of results that gained from Jefferis-Matusita method with those from SVM indicate that this method is much more operative than other ones for low discriminative data, and then its efficiency is high for producing geological maps.

Cites

References

Cite

APA: Copy

GODARZI MEHR, S., ABBASPOUR, R.A., AHADNEZHAD, V., & KHAKBAZ, B.. (2012). COMPARISON OF SUPPORT VECTOR MACHINE, NEURAL NETWORK, AND MAXIMUM LIKELIHOOD METHODS FOR THE SEPARATION OF LITHOLOGICAL UNITS. IRANIAN JOURNAL OF GEOLOGY, 6(22), 75-92. SID. https://sid.ir/paper/129265/en

Vancouver: Copy

GODARZI MEHR S., ABBASPOUR R.A., AHADNEZHAD V., KHAKBAZ B.. COMPARISON OF SUPPORT VECTOR MACHINE, NEURAL NETWORK, AND MAXIMUM LIKELIHOOD METHODS FOR THE SEPARATION OF LITHOLOGICAL UNITS. IRANIAN JOURNAL OF GEOLOGY[Internet]. 2012;6(22):75-92. Available from: https://sid.ir/paper/129265/en

IEEE: Copy

S. GODARZI MEHR, R.A. ABBASPOUR, V. AHADNEZHAD, and B. KHAKBAZ, “COMPARISON OF SUPPORT VECTOR MACHINE, NEURAL NETWORK, AND MAXIMUM LIKELIHOOD METHODS FOR THE SEPARATION OF LITHOLOGICAL UNITS,” IRANIAN JOURNAL OF GEOLOGY, vol. 6, no. 22, pp. 75–92, 2012, [Online]. Available: https://sid.ir/paper/129265/en

Related Journal Papers