Abstract
Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace clustering (MVKCSC) and random subspace MVKCSC (RSMVKCSC) are proposed. In order to combine spectral and spatial information to construct a region of competence (RoC), the multiview learning strategy is used in the general DES method. Compared with traditional clustering methods, the MVC can more effectively utilize multifeature information. Moreover, a new method of constructing the Laplacian matrix using kernel CR coefficients is proposed for clustering based on subspace clustering and CR theory. This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. In addition, to increase the diversity of samples, the random subspace method (RSM) and MVKCSC are combined for RMVKCSC. Moreover, the algorithm can obtain better clustering results by constraining samples and features simultaneously. The effectiveness of the proposed methods is validated using three hyperspectral data sets with few samples. The experimental results show that both DES-MVKCSC and DES-RSMVKCSC outperform their single classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art DES methods.
Highlights
Hyperspectral image provides abundant spectral information in hundreds of contiguous spectral bands [1]
The details of the proposed dynamic ensemble selection (DES)-multi-view kernel collaborative subspace clustering (MVKCSC) and DESRSMVKCSC algorithms are mainly described from three aspects: classifier pool construction, region of competence (RoC) division, classifier selection and classifier fusion
The random subspace method is proposed to be added to the MVKCSC algorithm, thereby reducing the dimensionality of features and increasing the diversity of classification results
Summary
Hyperspectral image provides abundant spectral information in hundreds of contiguous spectral bands [1]. Because a single classifier often cannot obtain an optimal classification result, some researchers have proposed to improve the classification accuracy of hyperspectral images based on ensemble learning methods [20] – [22]. Chen et al [37] proposed to use the SVM as a base classifier combined with a stacking model for hyperspectral image classification, and demonstrates that this method can effectively improve the classification accuracy. The base classifiers used in the above methods are all traditional machine learning classifiers, which do not have advantages when using a small training set These methods cannot solve limited label samples in hyperspectral classification. The method based on dynamic ensemble adopts the concept of the RoC It divides the testing data into multiple regions and finds a locally optimal classifier set for each region. Constrained the data at the feature and sample level simultaneously, which can provide better clustering results
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