Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices
This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and repr...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.224.3585 2023-05-15T18:16:39+02:00 Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices Leen-kiat Soh Costas Tsatsoulis The Pennsylvania State University CiteSeerX Archives 1999 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.3585 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.3585 Metadata may be used without restrictions as long as the oai identifier remains attached to it. text 1999 ftciteseerx 2016-01-07T18:25:21Z This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. We conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures. We showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, we developed three GLCM implementations and Text Sea ice Unknown |
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Open Polar |
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ftciteseerx |
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English |
description |
This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. We conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures. We showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, we developed three GLCM implementations and |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Leen-kiat Soh Costas Tsatsoulis |
spellingShingle |
Leen-kiat Soh Costas Tsatsoulis Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices |
author_facet |
Leen-kiat Soh Costas Tsatsoulis |
author_sort |
Leen-kiat Soh |
title |
Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices |
title_short |
Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices |
title_full |
Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices |
title_fullStr |
Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices |
title_full_unstemmed |
Texture Analysis of SAR Sea Ice Imagery using Gray Level Co-occurrence Matrices |
title_sort |
texture analysis of sar sea ice imagery using gray level co-occurrence matrices |
publishDate |
1999 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.3585 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.3585 |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766190418983124992 |