Classification of SAR images using a general and tractable multiplicative model
Among the frameworks for Synthetic Aperture Radar (SAR) image modelling and analysis, the multiplicative model is very accurate and successful. It is based on the assumption that the observed random field is the result of the product of two independent and unobserved random fields: X and Y. The rand...
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2003
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01431161_v24_n18_p3565_Mejail http://hdl.handle.net/20.500.12110/paper_01431161_v24_n18_p3565_Mejail |
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paper:paper_01431161_v24_n18_p3565_Mejail2023-06-08T15:11:45Z Classification of SAR images using a general and tractable multiplicative model Computer simulation Imaging systems Mathematical models Speckle Spurious signal noise Homogeneous areas Remote sensing backscatter geostatistics image analysis image classification remote sensing synthetic aperture radar Among the frameworks for Synthetic Aperture Radar (SAR) image modelling and analysis, the multiplicative model is very accurate and successful. It is based on the assumption that the observed random field is the result of the product of two independent and unobserved random fields: X and Y. The random field X models the terrain backscatter and, thus, depends only on the type of area to which each pixel belongs. The random field Y takes into account that SAR images are the result of a coherent imaging system that produces the well-known phenomenon called speckle noise, and that they are generated by performing an average of n statistically independent images (looks) in order to reduce the noise effect. There are various ways of modelling the random field X; recently the Γ- 1/2(α, γ) distribution was proposed. This, with the usual Γ1/2(n, n) distribution for the amplitude speckle, resulted in a new distribution for the return: the 0 A (α, γ, n) law. The parameters α and γ depend only on the ground truth, and n is the number of looks. The advantage of this distribution over the ones used in the past is that it models very well extremely heterogeneous areas like cities, as well as moderately heterogeneous areas like forests and homogeneous areas like pastures. As the ground data can be characterized by the parameters α and γ, their estimation in each pixel generates parameter maps that can be used as the input for classification methods. In this work, moment estimators are used on simulated and on real SAR images and, then, a supervised classification technique (Gaussian maximum likelihood) is performed and evaluated. Excellent classification results are obtained. 2003 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01431161_v24_n18_p3565_Mejail http://hdl.handle.net/20.500.12110/paper_01431161_v24_n18_p3565_Mejail |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Computer simulation Imaging systems Mathematical models Speckle Spurious signal noise Homogeneous areas Remote sensing backscatter geostatistics image analysis image classification remote sensing synthetic aperture radar |
| spellingShingle |
Computer simulation Imaging systems Mathematical models Speckle Spurious signal noise Homogeneous areas Remote sensing backscatter geostatistics image analysis image classification remote sensing synthetic aperture radar Classification of SAR images using a general and tractable multiplicative model |
| topic_facet |
Computer simulation Imaging systems Mathematical models Speckle Spurious signal noise Homogeneous areas Remote sensing backscatter geostatistics image analysis image classification remote sensing synthetic aperture radar |
| description |
Among the frameworks for Synthetic Aperture Radar (SAR) image modelling and analysis, the multiplicative model is very accurate and successful. It is based on the assumption that the observed random field is the result of the product of two independent and unobserved random fields: X and Y. The random field X models the terrain backscatter and, thus, depends only on the type of area to which each pixel belongs. The random field Y takes into account that SAR images are the result of a coherent imaging system that produces the well-known phenomenon called speckle noise, and that they are generated by performing an average of n statistically independent images (looks) in order to reduce the noise effect. There are various ways of modelling the random field X; recently the Γ- 1/2(α, γ) distribution was proposed. This, with the usual Γ1/2(n, n) distribution for the amplitude speckle, resulted in a new distribution for the return: the 0 A (α, γ, n) law. The parameters α and γ depend only on the ground truth, and n is the number of looks. The advantage of this distribution over the ones used in the past is that it models very well extremely heterogeneous areas like cities, as well as moderately heterogeneous areas like forests and homogeneous areas like pastures. As the ground data can be characterized by the parameters α and γ, their estimation in each pixel generates parameter maps that can be used as the input for classification methods. In this work, moment estimators are used on simulated and on real SAR images and, then, a supervised classification technique (Gaussian maximum likelihood) is performed and evaluated. Excellent classification results are obtained. |
| title |
Classification of SAR images using a general and tractable multiplicative model |
| title_short |
Classification of SAR images using a general and tractable multiplicative model |
| title_full |
Classification of SAR images using a general and tractable multiplicative model |
| title_fullStr |
Classification of SAR images using a general and tractable multiplicative model |
| title_full_unstemmed |
Classification of SAR images using a general and tractable multiplicative model |
| title_sort |
classification of sar images using a general and tractable multiplicative model |
| publishDate |
2003 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01431161_v24_n18_p3565_Mejail http://hdl.handle.net/20.500.12110/paper_01431161_v24_n18_p3565_Mejail |
| _version_ |
1768543029568733184 |