鲁棒多特征谱聚类的高光谱影像波段选择

传统谱聚类的高光谱影像波段选择模型中,采用的波段相似矩阵受到噪声或异常值的影响且仅能表征波段的单一相似特征,导致波段子集的选取结果受到限制。本文从波段选择的目的出发,提出鲁棒多特征谱聚类方法,整合多个特征的波段相似矩阵来形成综合相似矩阵以解决上述问题。该方法假设4种相似性度量包括光谱信息散度、光谱角度距离、波段相关性和拉普拉斯图谱能够共同揭示波段聚类的内在结构特征,通过构建低秩稀疏矩阵分解模型来表征单一相似矩阵与综合相似矩阵的内在关系。进一步,采用增强拉格朗日乘子算法来优化求解综合相似矩阵,利用常规谱聚类方法来聚合所有波段至不同的类别,并选取代表性波段。采用两个常用的高光谱影像数据,对比5种常用的波段选择方法来进行实验验证。实验结果表明,鲁棒多特征谱聚类方法优于改进稀疏子空间聚类、常规谱聚类方法和其他主流波段选择方法,而且计算效率较高。

Robust multi-feature spectral clustering for hyperspectral band selection

The Hughes problem together with strong intra-band correlations and massive data seriously hinders hyperspectral processing and further applications. Dimensionality reduction using band selection can be used to conquer the abovementioned problems and guarantee the application performance of hyperspectral data. In particular, spectral clustering is a typical method for high-dimensional hyperspectral data. This method finds clusters of all hyperspectral bands on the connected graph and selects the representatives. Unfortunately, the regular similarity measures are negatively affected by outliers or noise of hyperspectral data in measuring the similarity of different bands. They could also only represent one feature of band similarity and have respective limitations. Accordingly, the obtained similarity matrix could not represent the full information of band selection required and could not guarantee obtaining aimed bands from spectral clustering. Therefore, we propose a Robust Multifeature Spectral Clustering (RMSC) method to solve the two problems mentioned above and enhance the performance of hyperspectral band selection from spectral clustering.The RMSC combines multiple features of similarity measures for pairwise bands, namely, information entropy, band correlation, and band dissimilarity, to construct the integrated similarity matrix. It utilizes spectral information divergence to quantify the information entropy between pairwise bands. The coefficient correlation is utilized to measure the band correlations and construct the similarity matrix of band correlations. The Laplacian graph is also adopted to construct a similarity matrix and show the dissimilarity between different bands considering the inner clustering structure of all bands. The spectral angle distance matrix is constructed as well to reflect the similarity from the aspects of overall differences. The RMSC regards that each similarity matrix of all four features reflect the underlying true clustering information of all bands and has low-rank property. It formulates the estimation of combined dissimilarity matrix into a low-rank and sparse decomposition problem and utilizes the augmented Lagrangian multiplier to solve it. Thereafter, it implements the regular spectral clustering on the integrated similarity matrix and selects the representative bands from each cluster.Two hyperspectral datasets are used to design four groups of experiments and testify the performance of RMSC. Five state-of-the-art methods, namely, WaluDI, fast density-peak-based clustering, orthogonal projections based band selection, Improved Sparse Spectral Clustering (ISSC) and SC-SID, and support vector machine, are used to quantify the classification accuracy. Experimental results show that RSMC outperforms the five other band selection methods in overall classification accuracy with shorter computational time. The regularization parameter is insensitive to RMSC, and a small candidate could produce high classification accuracy.RMSC is better in selecting representative bands than current spectral clustering such as ISSC. It can also be a good choice in hyperspectral dimensionality reduction.