Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Jinliang An,Jinmei Guo,Yuzhen Song,Jinhui Lei,Xiangrong Zhang

doi:10.3390/rs11121485

Jinliang An, Jinmei Guo + Show 3 more

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https://doi.org/10.3390/rs11121485

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Abstract

Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches.

Highlights

By collecting hundreds of contiguous narrow spectral bands, hyperspectral images contain wealth of spectral information and have been applied in classification [1,2], target detection [3,4], etc. [5,6]
To overcome the drawbacks of available methods, an unsupervised tensor based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper
Classification Results of Different Reduced Dimensionality. It can be seen from the analysis of different reduced dimensionality that, when the reduced dimensionality is larger than 20, the proposed method achieves the best classification performance of all compared methods, while, when the reduced dimensionality k is less than 15, the accuracy performance of T-LMRD is worse than some of the compared methods. This is mainly due to the stacking strategy of all single low rank representations on a spectral domain possibly leading to a high dimensionality of multiscale low rank representation

Summary

Introduction

By collecting hundreds of contiguous narrow spectral bands, hyperspectral images contain wealth of spectral information and have been applied in classification [1,2], target detection [3,4], etc. [5,6]. Depending on whether labeled training samples are required or not, the dimensionality reduction algorithms can be classified into two major categories: supervised methods and unsupervised methods. The cost of hyperspectral image samples labeling is extremely high, so the unsupervised methods have attracted much more attention in hyperspectral image processing. With the advantage of capturing the global structure of the original hyperspectral images, low rank representation has attracted more and more attention [10,11,12,13,14,15], but the basic low rank representation methods need the vectoring processing to convert the 3-order hyperspectral images to vector samples. The vectoring processing makes the vector samples fail at capturing the spatial neighborhood structure information of hyperspectral images

Results

Discussion

Conclusion