Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels that correspond to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets.

翻译：航空或卫星拍摄的高光谱图像包含数百个光谱波段的信息，其中隐藏着可用于植被及其他材料分类的低维潜在结构。高光谱图像处理的难点在于：由于光谱分辨率与空间分辨率存在固有权衡，其空间尺度相对粗糙，导致单个像素可能对应包含多种材料的空间区域。本文提出基于扩散与体积最大化的图像聚类算法（D-VIC），用于解决无监督材料聚类问题。该算法通过将像素纯度直接纳入标记流程，对对应单一材料空间区域的像素赋予更高权重。在涵盖土地利用图及高度混合森林健康调查（以白蜡树枯梢病为例）的多类高光谱图像实验中，D-VIC展现出优于同类先进方法的性能，表明该算法能有效对光谱混合的高光谱数据集进行无监督材料聚类。