research-article

Polarimetric SAR Images Classification and Texture Features

Authors:

Boussad Azmedroub,

Mounira OuarzeddineAuthors Info & Claims

MedPRAI-2016: Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence

Pages 50 - 55

https://doi.org/10.1145/3038884.3038893

Published: 22 November 2016 Publication History

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Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) imagery classification is widely investigated, and there are a lot of proposed classifiers. The main issue in the classification of PolSAR images is the extraction of effective features that allow the preservation of the scattering mechanisms in order to give good results. In this paper we investigate the effectiveness of the textures features for polarimetric SAR images. We used for this analysis two classifiers, Feed Forward Neural Network (FNN) and the Maximum Likelihood (ML) Wishart. The textures extracted are the Gabor filters. The results show that textures add information to the polarimetric features, thus, allowing good classification results. In order to validate our experiment we used two PolSAR images RADARSAT-2, and AIRSAR C-band images of San Francisco.

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Cited By

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Imani M(2022)Entropy/anisotropy/alpha based 3DGabor filter bank for PolSAR image classificationGeocarto International10.1080/10106049.2022.214296337:27(18491-18519)Online publication date: 11-Nov-2022
https://doi.org/10.1080/10106049.2022.2142963
Xie CZhuang LHao MChen K(2021)Monitoring of Lake Area Changes from SAR Images Based on Convolutional Neural Networks and Markov Random Field2021 CIE International Conference on Radar (Radar)10.1109/Radar53847.2021.10027918(991-994)Online publication date: 15-Dec-2021
https://doi.org/10.1109/Radar53847.2021.10027918
Chen KHao MZhuang LXie CGuo J(2020)POLSAR Image Classification Based on SVM and WD-MRF Method2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)10.1109/ITOEC49072.2020.9141742(1631-1636)Online publication date: Jun-2020
https://doi.org/10.1109/ITOEC49072.2020.9141742

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Information

Published In

MedPRAI-2016: Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence

November 2016

163 pages

ISBN:9781450348768

DOI:10.1145/3038884

General Chairs:
Chawki Djeddi
Larbi Tebessi University, Algeria
,
Imran Siddiqi
Bahria University, Pakistan
,
Akram Bennour
Larbi Tebessi University, Algeria
,
Program Chairs:
Youcef Chibani
University of Science and Technology Houari Boumediene, Algeria
,
Haikal El Abed
Technical Trainers College, German International Cooperation, Kingdom of Saudi Arabia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2016

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MedPRAI-2016

MedPRAI-2016: Mediterranean Conference on Pattern Recognition and Artificial Intelligence

November 22 - 23, 2016

Tebessa, Algeria

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Cited By

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Imani M(2022)Entropy/anisotropy/alpha based 3DGabor filter bank for PolSAR image classificationGeocarto International10.1080/10106049.2022.214296337:27(18491-18519)Online publication date: 11-Nov-2022
https://doi.org/10.1080/10106049.2022.2142963
Xie CZhuang LHao MChen K(2021)Monitoring of Lake Area Changes from SAR Images Based on Convolutional Neural Networks and Markov Random Field2021 CIE International Conference on Radar (Radar)10.1109/Radar53847.2021.10027918(991-994)Online publication date: 15-Dec-2021
https://doi.org/10.1109/Radar53847.2021.10027918
Chen KHao MZhuang LXie CGuo J(2020)POLSAR Image Classification Based on SVM and WD-MRF Method2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)10.1109/ITOEC49072.2020.9141742(1631-1636)Online publication date: Jun-2020
https://doi.org/10.1109/ITOEC49072.2020.9141742

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Polarimetric SAR image classification by using artificial neural network

Speckle Reduction of Polarimetric SAR Image Based on ICA-SCS Algorithm

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