The accurate segmentation of remotely sensed hyperspectral images has widespread attention in the Earth observation and remote sensing communities. In the past decade, most of the efforts focus on the development of different supervised methods for hyperspectral image classification. Recently, the computer vision community is developing unsupervised methods that can adapt to new conditions without leveraging expensive supervision. In general, among unsupervised classification methods, sparse subspace clustering (SSC) is a popular tool that achieves good clustering results on experiments with real data. However, for the specific case of hyperspectral clustering, the SSC model does not take into account the spatial information of such images, which limits its discrimination capability and hampering the spatial homogeneity of the clustering results. As a solution, we propose to incorporate a regularization term to the SSC model, which takes into account the neighboring spatial information of spectral pixels in the scene. Specifically, the proposed method uses a three-dimensionall (3D) Gaussian filter to perform a 3D convolution on the sparse coefficients, obtaining a piecewise-smooth representation matrix that enforces an averaging constraint in the SSC optimization program. Extensive simulations demonstrate the effectiveness of the proposed method, achieving an overall accuracy of up to 99% in the selected hyperspectral remote sensing datasets.

中文翻译：

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使用3D正则化子空间聚类模型的高光谱图像分割

遥感高光谱图像的准确分割已在地球观测和遥感界引起广泛关注。在过去的十年中，大多数工作都集中在开发用于高光谱图像分类的不同监督方法上。最近，计算机视觉界正在开发不受监督的方法，这些方法可以适应新的条件而无需利用昂贵的监督。通常，在无监督分类方法中，稀疏子空间聚类（SSC）是一种流行的工具，可以在对真实数据进行的实验中获得良好的聚类结果。但是，对于高光谱聚类的特定情况，SSC模型未考虑此类图像的空间信息，这限制了其判别能力并妨碍了聚类结果的空间均匀性。作为解决方案，我们建议将正则化项合并到SSC模型中，该模型应考虑场景中光谱像素的相邻空间信息。具体而言，所提出的方法使用三维（3D）高斯滤波器对稀疏系数执行3D卷积，从而获得在SSC优化程序中实施平均约束的分段平滑表示矩阵。大量的仿真证明了该方法的有效性，在选定的高光谱遥感数据集中实现了高达99％的整体精度。所提出的方法使用三维（3D）高斯滤波器对稀疏系数执行3D卷积，获得在SSC优化程序中强制实施平均约束的分段平滑表示矩阵。大量的仿真证明了该方法的有效性，在选定的高光谱遥感数据集中实现了高达99％的整体精度。所提出的方法使用三维（3D）高斯滤波器对稀疏系数执行3D卷积，获得在SSC优化程序中强制实施平均约束的分段平滑表示矩阵。大量的仿真证明了该方法的有效性，在选定的高光谱遥感数据集中实现了高达99％的整体精度。