Abstract
In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.
Highlights
Hyperspectral sensors can acquire nearly continuous spectral bands with hundreds of channels to capture the diagnostic information of land-cover materials(Zhang, et al, 2014a), which opens up new possibilities for remote sensing applications, such as mineral exploration, fine agriculture, disaster monitoring, and so on (Landgrebe, 2002, Zhang, et al, 2014b)
In order to further improve the clustering performance, we combine the two schemes into a unified framework to obtain the kernel sparse subspace clustering algorithm (KSSC)-SMP algorithm, which can simultaneously deal with the complex nonlinear structure and utilize the spectral-spatial attributes of hyperspectral images (HSIs)
Main algorithm: 1) Construct the kernel sparse representation optimization model (4) and solve it to obtain the kernel sparse representation coefficient matrix C using alternating direction method of multipliers (ADMM); 2) Conduct the spatial max pooling operation on C to obtain the pooling coefficient matrix; 3) Construct the similarity graph with the pooling coefficient matrix; 4) Apply spectral clustering to the similarity graph to obtain the final clustering results; Output: A 2-D matrix which records the labels of the clustering result of the HSI
Summary
Hyperspectral sensors can acquire nearly continuous spectral bands with hundreds of channels to capture the diagnostic information of land-cover materials(Zhang, et al, 2014a), which opens up new possibilities for remote sensing applications, such as mineral exploration, fine agriculture, disaster monitoring, and so on (Landgrebe, 2002, Zhang, et al, 2014b). In recent years, researchers have begun to develop spectral-spatial clustering methods which consider spectral measurements together with spatial information to improve the clustering performance, such as FCM_S1 (Chen, and Zhang, 2004), and kmeans_S (Luo, et al, 2003) These methods still have limited clustering performance due to large spectral variability of HSIs. The sparse subspace clustering (SSC) algorithm was recently proposed (Elhamifar, and Vidar, 2013), and has achieved great success in the face recognition and motion segmentation fields. In this paper, a novel kernel sparse subspace clustering algorithm with spatial max pooling operation (KSSCSMP) for hyperspectral remote sensing imagery was proposed, which simultaneously explores the nonlinear structure and the inherent spectral-spatial attributes of HSIs. Firstly, we map the feature points from the original feature space to a higherdimensional space with the kernel strategy to make the feature points linearly inseparable. In order to fully exploit the spectral-spatial discrimination information of HSIs and the potential of the SSC model, the spatial max pooling operation is introduced to incorporate the spatial information to improve the clustering performance and guarantee spatial homogeneity of the clustering result
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