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Darvishnezhad, M., Sebt, . (2022). Feature Selection Method Based on Mutual Information for Polarimetric Synthetic Aperture Radar (PolSAR) Image Classification. Electronic and Cyber Defense, 10(1), 35-55.
Mohsen Darvishnezhad; ُMohammad Ali Sebt. "Feature Selection Method Based on Mutual Information for Polarimetric Synthetic Aperture Radar (PolSAR) Image Classification". Electronic and Cyber Defense, 10, 1, 2022, 35-55.
Darvishnezhad, M., Sebt, . (2022). 'Feature Selection Method Based on Mutual Information for Polarimetric Synthetic Aperture Radar (PolSAR) Image Classification', Electronic and Cyber Defense, 10(1), pp. 35-55.
Darvishnezhad, M., Sebt, . Feature Selection Method Based on Mutual Information for Polarimetric Synthetic Aperture Radar (PolSAR) Image Classification. Electronic and Cyber Defense, 2022; 10(1): 35-55.

Feature Selection Method Based on Mutual Information for Polarimetric Synthetic Aperture Radar (PolSAR) Image Classification

رادار
Article 7, Volume 10, Issue 1 - Serial Number 27, July 2022, Pages 35-55    PDF (1.74 M)
Document Type: Original Article
Authors
Mohsen Darvishnezhad* 1; ُMohammad Ali Sebt2
1PhD student, K.N.Toosi University of Tech., Tehran, Iran
2Associate Professor, K.N.Toosi University of Tech., Tehran, Iran
Receive Date: 13 April 2022,  Revise Date: 07 August 2022,  Accept Date: 24 August 2022 
Abstract
In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been cited as one of the most important applications of images classification. Therefore, in order to achieve the best result of PolSAR image classification in this article, a new feature selection method will be proposed based on mutual information theory. In the proposed method, the features that are extracted from PolSAR images will be used to obtain an initial class map. Then, each feature will be ranked based on mutual information. In the next step, the best features will be selected by using the proposed method accurately. The results that are obtained on the real PolSAR image of the Flevoland area prove an increase in the classification accuracy of the proposed method compared with other methods that are used in this research.
Keywords
Classification; PolSAR; Feature Selection; Synthetic Aperture Radar
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