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:从飞机或卫星上拍摄的超光谱图像含有数百个光谱带的信息,其中潜伏的低维结构可用于植被和其他材料的分类,使用超光谱图像的缺点是,由于光谱分辨率和空间分辨率之间的内在权衡,它们的空间尺度相对粗略,这意味着单像素可能与含有多种材料的空间区域相对应。本文章介绍了用于解决这个问题的未经监督的材料集群的传播和量最大化图像集成算法(D-VIC),通过直接将像素纯度纳入标签程序,D-VIC对与仅包含单一材料的空间区域相对应的像素给予更大的权重。D-VIC在一系列超光谱图像的广泛实验中显示,超光谱图像包括土地使用图和高度混合的森林健康调查(在炭死后疾病背景下),意味着它完全能够用于光谱混合超光谱数据集的未经超超强材料集成。