Classification of polarimetric radar images based on SVM and BGSA

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REZAEI YASER; Rezaee Alirezae; Darakeh Fateme; Azarakhsh Zeynab

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Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2021 | Volume:18 | Issue:1 (47) Page(s): 87-102

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Information Journal Paper

Title

Classification of polarimetric radar images based on SVM and BGSA

Author(s)

REZAEI YASER | Rezaee Alirezae | Darakeh Fateme | Azarakhsh Zeynab | Issue Writer Certificate

Keywords

Feature Selection

Image classification

support vector machine

optimization

binary gravitational search algorithm

polarimetric radar

Abstract

Classification of land cover is one of the most important applications of radar polarimetry images. The purpose of Image classification is to classify image pixels into different classes based on vector properties of the extractor. Radar imaging systems provide useful information about ground cover by using a wide range of electromagnetic waves to image the Earthchr('39')s surface. The purpose of this study is to present an optimal method for classifying polarimetric radar images. The proposed method is a combination of support vector machine and binary gravitational search optimization algorithm. In this regard, first a set of polarimetric features including original data values, target parsing features, and SAR separators are extracted from the images. Then, in order to select the appropriate features and determine the optimal parameters for the support vector machine classifier, the binary gravitational search algorithm is used. In order to achieve a classification system with high classification accuracy, the optimal values of the model parameters and a subset of the optimal properties are selected simultaneously. The results of the implementation of the proposed algorithm are compared with two states, taking into account all the selected features, and the genetic algorithm, the results of zoning for the three regions are examined. The separation of areas for the San Francisco and Manila regions, and the detection of oil slicks in the ocean surface of the Philippines, have been evaluated. The comparison with the genetic algorithm was approximately between 6% to 12% and the comparison with the presence of all features was between 13% and 20%. For the San Francisco area, the number of extraction properties was 101, which was selected using the proposed 47 optimal properties algorithm. For the city of Manila, after applying the algorithm, 31 optimal features have been selected from 65 features. For the oil slick of the city of the Philippines, we have reached the stated accuracy by selecting 33 features from 69 features, for the first two regions the number of initial population is 50 and the repetition period is 30, and for the third region with 30 initial population and the repetition period is 10.

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APA: Copy

REZAEI, YASER, Rezaee, Alirezae, Darakeh, Fateme, & Azarakhsh, Zeynab. (2021). Classification of polarimetric radar images based on SVM and BGSA. SIGNAL AND DATA PROCESSING, 18(1 (47) ), 87-102. SID. https://sid.ir/paper/960921/en

Vancouver: Copy

REZAEI YASER, Rezaee Alirezae, Darakeh Fateme, Azarakhsh Zeynab. Classification of polarimetric radar images based on SVM and BGSA. SIGNAL AND DATA PROCESSING[Internet]. 2021;18(1 (47) ):87-102. Available from: https://sid.ir/paper/960921/en

IEEE: Copy

YASER REZAEI, Alirezae Rezaee, Fateme Darakeh, and Zeynab Azarakhsh, “Classification of polarimetric radar images based on SVM and BGSA,” SIGNAL AND DATA PROCESSING, vol. 18, no. 1 (47) , pp. 87–102, 2021, [Online]. Available: https://sid.ir/paper/960921/en

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