IMPROVING THE URBAN AREA CLASSIFICATION USING RADAR POLARIMETRIC DATA AND MULTIOBJECTIVE OPTIMIZATION METHODS

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.

SALEHI M.; MAGHSOUDI Y.; SAHEBI M.R.

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

Journal Paper

Paper Information

Journal: JOURNAL OF RADAR Year:2014 | Volume:1 | Issue:2 (SERIAL NO. 2) Page(s): 45-56

Download Full-Text

Persian Verion

View:

998

Download:

Cites:

Information Journal Paper

Title

IMPROVING THE URBAN AREA CLASSIFICATION USING RADAR POLARIMETRIC DATA AND MULTIOBJECTIVE OPTIMIZATION METHODS

Author(s)

SALEHI M. | MAGHSOUDI Y. | SAHEBI M.R. | Issue Writer Certificate

Keywords

POLARIMETRIC DATAQ2

FEATURE SELECTIONQ1

CLASSIFICATIONQ2

MULTIOBJECTIVE OPTIMIZATIONQ2

SUPPORT VECTOR MACHINE (SVM)Q2

ADAPTIVE NEURO FUZZYQ2

INFERENCE SYSTEM (ANFIS)Q2

Abstract

Land cover CLASSIFICATION is one of the most important applications of polarimetric radar images, especially in urban areas. There are numerous features that can be extracted from these images for the use of their high potential, hence FEATURE SELECTION plays an important role in PolSAR image CLASSIFICATION. In this study, three main steps are used to improve the CLASSIFICATION: 1) feature extraction in the form of three categories, namely original data features, target decomposition features, and SAR discriminators; 2) selection of minimum number of features to achieve the high CLASSIFICATION accuracy; and 3) CLASSIFICATION using the best subset of features. In the proposed methods, NSGA-II MULTIOBJECTIVE OPTIMIZATION algorithm is employed as the search tool and Support Vector Machine (SVM) or ADAPTIVE NEURO FUZZY Inference System (ANFIS) is used in the evaluation step. The implementation results on the Radarsat-2 San Francisco Bay image showed that the proposed methods outperform the other approaches tested against them.

Cites

No record.

References

No record.

Cite

APA: Copy

SALEHI, M., MAGHSOUDI, Y., & SAHEBI, M.R.. (2014). IMPROVING THE URBAN AREA CLASSIFICATION USING RADAR POLARIMETRIC DATA AND MULTIOBJECTIVE OPTIMIZATION METHODS. JOURNAL OF RADAR, 1(2 (SERIAL NO. 2)), 45-56. SID. https://sid.ir/paper/244124/en

Vancouver: Copy

SALEHI M., MAGHSOUDI Y., SAHEBI M.R.. IMPROVING THE URBAN AREA CLASSIFICATION USING RADAR POLARIMETRIC DATA AND MULTIOBJECTIVE OPTIMIZATION METHODS. JOURNAL OF RADAR[Internet]. 2014;1(2 (SERIAL NO. 2)):45-56. Available from: https://sid.ir/paper/244124/en

IEEE: Copy

M. SALEHI, Y. MAGHSOUDI, and M.R. SAHEBI, “IMPROVING THE URBAN AREA CLASSIFICATION USING RADAR POLARIMETRIC DATA AND MULTIOBJECTIVE OPTIMIZATION METHODS,” JOURNAL OF RADAR, vol. 1, no. 2 (SERIAL NO. 2), pp. 45–56, 2014, [Online]. Available: https://sid.ir/paper/244124/en

Related Journal Papers