Zhu Xing-Hui, Zhou Yi, Yang Meng-Long, Deng Yang-Jun
College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China.
Sensors (Basel). 2022 Aug 7;22(15):5906. doi: 10.3390/s22155906.
Hyperspectral image (HSI) clustering is a challenging task, whose purpose is to assign each pixel to a corresponding cluster. The high-dimensionality and noise corruption are two main problems that limit the performance of HSI clustering. To address those problems, this paper proposes a projected clustering with a spatial-spectral constrained adaptive graph (PCSSCAG) method for HSI clustering. PCSSCAG first constructs an adaptive adjacency graph to capture the accurate local geometric structure of HSI data adaptively. Then, a spatial-spectral constraint is employed to simultaneously explore the spatial and spectral information for reducing the negative influence on graph construction caused by noise. Finally, projection learning is integrated into the spatial-spectral constrained adaptive graph construction for reducing the redundancy and alleviating the computational cost. In addition, an alternating iteration algorithm is designed to solve the proposed model, and its computational complexity is theoretically analyzed. Experiments on two different scales of HSI datasets are conducted to evaluate the performance of PCSSCAG. The associated experimental results demonstrate the superiority of the proposed method for HSI clustering.
高光谱图像（HSI）聚类是一项具有挑战性的任务，其目的是将每个像素分配到相应的聚类中。高维度和噪声干扰是限制HSI聚类性能的两个主要问题。为了解决这些问题，本文提出了一种用于HSI聚类的具有空间光谱约束自适应图的投影聚类（PCSSCAG）方法。PCSSCAG首先构建一个自适应邻接图，以自适应地捕捉HSI数据的准确局部几何结构。然后，采用空间光谱约束来同时探索空间和光谱信息，以减少噪声对图构建的负面影响。最后，将投影学习集成到空间光谱约束自适应图构建中，以减少冗余并减轻计算成本。此外，设计了一种交替迭代算法来求解所提出的模型，并对其计算复杂度进行了理论分析。在两个不同规模的HSI数据集上进行了实验，以评估PCSSCAG的性能。相关实验结果证明了所提出方法在HSI聚类方面的优越性。
