POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest a...

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Main Authors: S. Rouabah, M. Ouarzeddine, B. Azmedroub
Format: Article
Language:English
Published: Copernicus Publications 2016-06-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/341/2016/isprs-archives-XLI-B7-341-2016.pdf
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spelling doaj-5925afae4eaf4722aebc65f38bc3aafd2020-11-24T20:55:00ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B734134510.5194/isprs-archives-XLI-B7-341-2016POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITIONS. Rouabah0M. Ouarzeddine1B. Azmedroub2Electronic and Computing Faculty, USTHB, 16111 El Alia, Bab Ezzouar, AlgeriaElectronic and Computing Faculty, USTHB, 16111 El Alia, Bab Ezzouar, AlgeriaElectronic and Computing Faculty, USTHB, 16111 El Alia, Bab Ezzouar, AlgeriaDue to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/341/2016/isprs-archives-XLI-B7-341-2016.pdf
collection DOAJ
language English
format Article
sources DOAJ
author S. Rouabah
M. Ouarzeddine
B. Azmedroub
spellingShingle S. Rouabah
M. Ouarzeddine
B. Azmedroub
POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet S. Rouabah
M. Ouarzeddine
B. Azmedroub
author_sort S. Rouabah
title POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
title_short POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
title_full POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
title_fullStr POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
title_full_unstemmed POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION
title_sort polarimetric sar data gmm classification based on improved freeman incoherent decomposition
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2016-06-01
description Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/341/2016/isprs-archives-XLI-B7-341-2016.pdf
work_keys_str_mv AT srouabah polarimetricsardatagmmclassificationbasedonimprovedfreemanincoherentdecomposition
AT mouarzeddine polarimetricsardatagmmclassificationbasedonimprovedfreemanincoherentdecomposition
AT bazmedroub polarimetricsardatagmmclassificationbasedonimprovedfreemanincoherentdecomposition
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