Polarimetric SAR feature selection using a genetic algorithm
(2011) In Canadian Journal of Remote Sensing 37(1). p.27-36- Abstract
- One of the main applications of polarimetric synthetic aperture radar (POLSAR) images is terrain classification. In this study, an algorithm is presented to extract optimized features of POLSAR images that are required for classification. The proposed algorithm involves three main steps: (i) feature extraction using decomposition algorithms, including both coherent and incoherent decomposition algorithms; (ii) feature selection using a combination of a genetic algorithm (GA) and an artificial neural network (ANN); and (iii) image classification using the neural network. The algorithm is applied to a data set composed of different land cover elements, such as manmade objects, oceans, forests, and vegetation. The classification results... (More)
- One of the main applications of polarimetric synthetic aperture radar (POLSAR) images is terrain classification. In this study, an algorithm is presented to extract optimized features of POLSAR images that are required for classification. The proposed algorithm involves three main steps: (i) feature extraction using decomposition algorithms, including both coherent and incoherent decomposition algorithms; (ii) feature selection using a combination of a genetic algorithm (GA) and an artificial neural network (ANN); and (iii) image classification using the neural network. The algorithm is applied to a data set composed of different land cover elements, such as manmade objects, oceans, forests, and vegetation. The classification results obtained by the GA-based feature selection method exhibit the highest accuracy. The best features from the extracted features were identified and used in the classification based on the proposed algorithm. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4779445
- author
- Hadadi, G ; Sahebi, Mahmoudreza and Mansourian, Ali LU
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Remote sensing, SAR, Genetic algorithm (GA), Artificial Intelligence (AI)
- in
- Canadian Journal of Remote Sensing
- volume
- 37
- issue
- 1
- pages
- 27 - 36
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:84863336517
- ISSN
- 1712-7971
- language
- English
- LU publication?
- no
- id
- ca8c6f4a-914b-4e22-b12b-04ffbfdc3490 (old id 4779445)
- date added to LUP
- 2016-04-01 10:53:46
- date last changed
- 2023-09-05 14:11:43
@article{ca8c6f4a-914b-4e22-b12b-04ffbfdc3490,
abstract = {{One of the main applications of polarimetric synthetic aperture radar (POLSAR) images is terrain classification. In this study, an algorithm is presented to extract optimized features of POLSAR images that are required for classification. The proposed algorithm involves three main steps: (i) feature extraction using decomposition algorithms, including both coherent and incoherent decomposition algorithms; (ii) feature selection using a combination of a genetic algorithm (GA) and an artificial neural network (ANN); and (iii) image classification using the neural network. The algorithm is applied to a data set composed of different land cover elements, such as manmade objects, oceans, forests, and vegetation. The classification results obtained by the GA-based feature selection method exhibit the highest accuracy. The best features from the extracted features were identified and used in the classification based on the proposed algorithm.}},
author = {{Hadadi, G and Sahebi, Mahmoudreza and Mansourian, Ali}},
issn = {{1712-7971}},
keywords = {{Remote sensing; SAR; Genetic algorithm (GA); Artificial Intelligence (AI)}},
language = {{eng}},
number = {{1}},
pages = {{27--36}},
publisher = {{Taylor & Francis}},
series = {{Canadian Journal of Remote Sensing}},
title = {{Polarimetric SAR feature selection using a genetic algorithm}},
volume = {{37}},
year = {{2011}},
}