Multi-Scale Superpixels Dimension Reduction Hyperspectral Image Classification Algorithm Based on Low Rank Sparse Representation Joint Hierarchical Recursive Filtering

Abstract

The original Hyperspectral image (HSI) has different degrees of Hughes phenomenon and mixed noise, leading to the decline of classification accuracy. To make full use of the spatial-spectral joint information of HSI and improve the classification accuracy, a novel dual feature extraction framework joint transform domain-spatial domain filtering based on multi-scale-superpixel-dimensionality reduction (LRS-HRFMSuperPCA) is proposed. Our framework uses the low-rank structure and sparse representation of HSI to repair the unobserved part of the original HSI caused by noise and then denoises it through a block-matching 3D algorithm. Next, the dimension of the reconstructed HSI is reduced by principal component analysis (PCA), and the dimensions of the reduced images are segmented by multi-scale entropy rate superpixels. All the principal component images with superpixels are projected into the reconstructed HSI in parallel. Secondly, PCA is once again used to reduce the dimension of all HSIs with superpixels in scale with hyperpixels. Moreover, hierarchical domain transform recursive filtering is utilized to obtain the feature images; ultimately, the decision fusion strategy based on a support vector machine (SVM) is used for classification. According to the Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient on the three datasets (Indian Pines, University of Pavia and Salinas), the experimental results have shown that our proposed method outperforms other state-of-the-art methods. The conclusion is that LRS-HRFMSuperPCA can denoise and reconstruct the original HSI and then extract the space-spectrum joint information fully.

Keywords

• hyperspectral image
• low rank sparse representation
• entropy rate superpixel segmentation
• principal component analysis
• domain transform recursive filtering
• decision fusion