利用Haralick特征对射电星系形态的快速分类
Rapid sorting of radio galaxy morphology using Haralick features
摘要：
我们演示了使用Haralick功能的射电星系的自动分类。这组13个Haralick特征代表了图像纹理的一个非常紧凑的非参数表示，并且使用灰度级共生矩阵（GLCM）直接从图像中计算。GLCM是图像中相邻像素的强度之间的关系的编码。使用LOFAR两米巡天（LoTSS）的第一次数据发布中检测到的10，000个源，我们证明了Haralick特征是射电星系形态的高效，旋转不变的描述符。在计算LoTSS源的Haralick特征后，我们采用快速基于密度的层次聚类算法HDBSCAN将射电源分组为一系列形态类，说明了一种简单的方法来分类和标记大样本中的新的，看不见的星系。通过采用“软”聚类方法，我们可以为每个星系分配属于给定星系团的概率，从而根据形态特征的组合更灵活地选择星系，并轻松识别离群值：那些属于Haralick空间中任何星系团的概率较低的物体。虽然我们的演示侧重于射电星系，但Haralick功能可以计算任何图像，使这种方法也与大型光学成像星系调查有关。
英文摘要：
We demonstrate the use of Haralick features for the automated classification of radio galaxies. The set of thirteen Haralick features represent an extremely compact non-parametric representation of image texture, and are calculated directly from imagery using the Grey Level Co-occurrence Matrix (GLCM). The GLCM is an encoding of the relationship between the intensity of neighbouring pixels in an image. Using 10,000 sources detected in the first data release of the LOFAR Two-metre Sky Survey (LoTSS), we demonstrate that Haralick features are highly efficient, rotationally invariant descriptors of radio galaxy morphology. After calculating Haralick features for LoTSS sources, we employ the fast density-based hierarchical clustering algorithm HDBSCAN to group radio sources into a sequence of morphological classes, illustrating a simple methodology to classify and label new, unseen galaxies in large samples. By adopting a 'soft' clustering approach, we can assign each galaxy a probability of belonging to a given cluster, allowing for more flexibility in the selection of galaxies according to combinations of morphological characteristics and for easily identifying outliers: those objects with a low probability of belonging to any cluster in the Haralick space. Although our demonstration focuses on radio galaxies, Haralick features can be calculated for any image, making this approach also relevant to large optical imaging galaxy surveys.
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