ABSTRACT Hyperspectral image clustering is usually used for unsupervised learning in different applications. However, the traditional clustering methods have not been considered the complex relationships among neighbouring pixels. The Albedo and Shading elements can define pixel values in the HyperSpectral Images (HSIs). In HSIs, features are different from each other because of their natural physical characteristics and the physical nature of different image features can be described by the Albedo element. Therefore, in this paper, we generate the natural Albedo feature of the HSIs by applying Albedo recovery step to exploit main information from HSIs. Then, we utilized the Fuzzy C-means clustering method to cluster the natural Albedo dataset. In this paper, we propose a novel accurate Albedo Recovery based Fuzzy C-Means (ARFCM) method to cluster HSIs. In the dataset, each feature vector is processed by the Albedo recovery step to create a new feature vector. This new feature vector can describe the dataset better than the original one. Comparing clustering methods as one of the powerful clustering algorithms are widely used in the remote sensing fields of studying. The experiments conducted on several benchmark datasets demonstrated that the proposed clustering method achieves higher performance than other methods and present the efficiency and effectiveness of the proposed method. The results of experiments over different HSI datasets indicated that the proposed method could produce reliable and suitable results compared to the other methods. This shows the robustness of the proposed ARFCM algorithm over the various HSI datasets. Other methods may provide a good response in a given dataset and do not perform well in the other data. Consequently, the ARFCM method, regardless of the study area characteristics and the sensor features, always renders remarkable clustering accuracy.

中文翻译：

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具有反照率恢复模糊 C 均值的高光谱图像聚类

摘要 高光谱图像聚类通常用于不同应用中的无监督学习。然而，传统的聚类方法没有考虑到相邻像素之间的复杂关系。Albedo 和 Shading 元素可以定义高光谱图像 (HSI) 中的像素值。在HSI中，特征由于其自然物理特性而彼此不同，不同图像特征的物理性质可以用Albedo元素来描述。因此，在本文中，我们通过应用反照率恢复步骤来利用来自 HSI 的主要信息来生成 HSI 的自然反照率特征。然后，我们利用模糊 C 均值聚类方法对自然反照率数据集进行聚类。在本文中，我们提出了一种新颖的基于反照率恢复的模糊 C 均值 (ARFCM) 方法来聚类 HSI。在数据集中，每个特征向量都由反照率恢复步骤处理以创建一个新的特征向量。这个新的特征向量可以比原始特征向量更好地描述数据集。比较聚类方法作为一种强大的聚类算法被广泛应用于遥感研究领域。在几个基准数据集上进行的实验表明，所提出的聚类方法比其他方法具有更高的性能，并展示了所提出方法的效率和有效性。在不同 HSI 数据集上的实验结果表明，与其他方法相比，所提出的方法可以产生可靠和合适的结果。这显示了所提出的 ARCM 算法在各种 HSI 数据集上的稳健性。其他方法可能在给定的数据集中提供良好的响应，但在其他数据中表现不佳。因此，无论研究区域特征和传感器特征如何，ARFCM 方法始终具有显着的聚类精度。