Multifrequency Full Polarimetric SAR Classification with Multiple Sources of Statistical Evidence

Frery, Alejandro C.; Correia, Antonio Henrique; Freitas, Corina da Costa

doi:10.1109/igarss.2006.1076

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…In order to model extremely heterogeneous areas (such as urban areas), Frery et al (1997) and Freitas et al (2005) developed the G distribution. The G 0 distribution, a special case of the G distribution, has been introduced to the classification of PolSAR images (Frery et al 2006).…”

Section: Introductionmentioning

confidence: 99%

A novel measure of target scattering randomness with application to polarimetric SAR image classification

Zhao

Kuang

Zhou³

et al. 2011

International Journal of Remote Sensing

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Section: Introductionmentioning

confidence: 99%

A novel measure of target scattering randomness with application to polarimetric SAR image classification

Zhao

Kuang

Zhou³

et al. 2011

International Journal of Remote Sensing

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“…Radar signals at different wavelengths exhibit different sensitivities to ground features [43,44]. Thus, combining multiple bands might be helpful for ground imaging.…”

Section: Contribution Of Multi-frequency Datasetmentioning

confidence: 99%

Improved POLSAR Image Classification by the Use of Multi-Feature Combination

2015

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Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C-and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas.

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“…Furthermore, the distribution can not only describing well the data which the K distribution explains, but also explaining extremely heterogeneous regions. Frery, 2006 adopts the G0 distribution to feature classification of multilook fully polarimetric SAR data by ICM. The G0 distribution provides a good and tractable description of extremely heterogeneous areas in PolSAR imagery, it is a special form of G model (Freitas , 2005;Gambini J, 2008.…”

Section: Introductionmentioning

confidence: 99%

G0-Wishart Distribution Based Classification From Polarimetric Sar Images

Zhao

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

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Multifrequency Full Polarimetric SAR Classification with Multiple Sources of Statistical Evidence

Cited by 4 publications

References 9 publications

A novel measure of target scattering randomness with application to polarimetric SAR image classification

A novel measure of target scattering randomness with application to polarimetric SAR image classification

Improved POLSAR Image Classification by the Use of Multi-Feature Combination

G0-Wishart Distribution Based Classification From Polarimetric Sar Images

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