Abstract
Hyperspectral image (HSI) clustering has drawn increasing attention due to its challenging work with respect to the curse of dimensionality. In this paper, we propose a novel class probability propagation of supervised information based on sparse subspace clustering (CPPSSC) algorithm for HSI clustering. Firstly, we estimate the class probability of unlabeled samples by way of partial known supervised information, which can be addressed by sparse representation-based classification (SRC). Then, we incorporate the class probability into the traditional sparse subspace clustering (SSC) model to obtain a more accurate sparse representation coefficient matrix accompanied by obvious block diagonalization, which will be used to build the similarity matrix. Finally, the cluster results can be obtained by applying the spectral clustering on similarity matrix. Extensive experiments on a variety of challenging data sets illustrate that our proposed method is effective.
Highlights
Hyperspectral images (HSIs) can provide more detailed information for land-over classification and clustering with hundreds of spectral bands for each pixel [1,2,3,4]
Before performing the subspace clustering (SSC) algorithm, each pixel can be treated as a d-dimensional vector where d is the number of spectral bands [38] and the 3D HSI data Y ∈ RM×N×d must be translated into 2D matrix
The HSI data can be denoted by a 2D matrix Y = [y1, y2, · · ·, yMN]Y ∈ Rd×MN, where M represents the width of the HSI data and N is on behalf of height of the HSI data
Summary
Hyperspectral images (HSIs) can provide more detailed information for land-over classification and clustering with hundreds of spectral bands for each pixel [1,2,3,4]. Yang et al [33] have proposed a new semi-supervised low-rank representation (SSLRR) graph, which uses the calculated LRR coefficients of both labeled and unlabeled samples as the graph weights It can capture the structure of data and implement more robust subspace clustering. The proposed method incorporates supervised information into the SSC framework by exploring class relationship among the data samples, which can obtain the more accurate sparse coefficient matrix. This model can be better encouraged to assign more similar elements into corresponding class Such prior information can better capture the subspace structure of data, which can improve the self-expressiveness property of the samples and preserve the subspace-sparse representation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
