Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization for Hyperspectral Image Classification

Low-Rank and Sparse Representation (LRSR) method has gained popularity in Hyperspectral Image (HSI) processing. However, existing LRSR models rarely exploited spectral-spatial classification of HSI. In this paper, we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regu...

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Bibliographic Details
Published in:Journal of Geodesy and Geoinformation Science 2022-03, Vol.5 (1), p.73-90
Main Authors: Xue, Zhaohui, Nie, Xiangyu
Format: Article
Language:English
Subjects:
Classification
Data structures
hyperspectral image (hsi)|spectral-spatial classification|low-rank and sparse representation (lrsr)|adaptive neighborhood regularization (anr)
Hyperspectral imaging
Image classification
Neighborhoods
Pixels
Regularization
Representations
Spatial data
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Summary:Low-Rank and Sparse Representation (LRSR) method has gained popularity in Hyperspectral Image (HSI) processing. However, existing LRSR models rarely exploited spectral-spatial classification of HSI. In this paper, we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization (LRSR-ANR) method for HSI classification. In the proposed method, we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously. The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers (M-ADMM), which converges faster than ADMM. Then to incorporate the spatial information, an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood. Lastly, the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error. Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.
ISSN:2096-5990
2096-1650
DOI:10.11947/j.JGGS.2022.0108