Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices
Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices
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DOI：
10.1109/36.752194
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发表时间：
1999-03-01
影响因子：
8.2
通讯作者：
Tsatsoulis, C
中科院分区：
文献类型：
--
作者：
Soh, LK;Tsatsoulis, C
本文介绍了利用100m ERS-1合成孔径雷达(SAR)图像绘制海冰图案(纹理)的初步研究。我们使用灰度共生矩阵(GLCM)来定量评估纹理参数和表示，并确定哪些参数值和表示最适合绘制海冰纹理。我们通过考察不同纹理描述符(如熵)在表示不同海冰纹理时的影响，对图像的量化级别以及GLCM的位移和方向值进行了实验，结果表明，纹理映射不需要图像的完整灰度表示，纹理表示不需要8级量化表示，纹理测量中的位移因子比方向更重要。此外，我们开发了三个GLCM实现，并用一个有监督的贝叶斯分类器对海冰纹理环境进行了评估。这项实验的结论是，在表示海冰纹理方面，最好的GLCM实现是利用一系列的位移值，以便充分捕捉海冰的微观纹理和宏观纹理，这些发现定义了最适合使用GLCM进行SAR海冰纹理分析的量化、位移和方向值。
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 evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured, These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM.