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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Haralick Texture Features Expanded Into The Spectral Domain
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Haralick Texture Features Expanded Into The Spectral Domain

机译：Haralick纹理特征扩展到光谱域

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Robert M. Haralick, et. al., described a technique for computing texture features based on gray-level spatial dependencies using a Gray Level Co-occurrence Matrix (GLCM). The traditional GLCM process quantizes a grayscale image into a small number of discrete gray-level bins. The number and arrangement of spatially co-occurring gray-levels in an image is then statistically analyzed. The output of the traditional GLCM process is a gray-scale image with values corresponding to the intensity of the statistical measure. A method to calculate Spectral Texture is modeled on Haralick's texture features. This Spectral Texture Method uses spectral-similarity spatial dependencies (rather than gray-level spatial dependencies). In the Spectral Texture Method, a spectral image is quantized based on discrete spectral angle ranges. Each pixel in the image is compared to an exemplar spectrum, and a quantized image is created in which pixel values correspond to a spectral similarity value. Statistics are calculated on spatially co-occurring spectral-similarity values. Comparisons between Haralick Texture Features and the Spectral Texture Method results are made, and possible uses of Spectral Texture features are discussed.
机译：Robert M. Haralick等。等人描述了一种使用灰度共生矩阵（GLCM）基于灰度空间依赖性来计算纹理特征的技术。传统的GLCM处理将灰度图像量化为少量离散的灰度等级。然后，对图像中空间上同时出现的灰度级的数量和排列进行统计分析。传统GLCM过程的输出是灰度图像，其值对应于统计量的强度。一种基于Haralick的纹理特征计算光谱纹理的方法。该光谱纹理方法使用光谱相似性空间依赖性（而不是灰度空间依赖性）。在“光谱纹理方法”中，基于离散光谱角度范围对光谱图像进行量化。将图像中的每个像素与示例光谱进行比较，并创建量化图像，其中像素值对应于光谱相似度值。统计是在空间上同时出现的光谱相似度值上计算的。比较了Haralick纹理特征和光谱纹理方法的结果，并讨论了光谱纹理特征的可能用途。

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