Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Remote Sensing

Jinmei Guo¹  Xiangrong Zhang¹  Jinliang An²  Jinhui Lei²  Yuzhen Song² 

[1] School of Artificial Intelligence, Xidian University, Xi’an 710071, China;School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China;

关键词: dimensionality reduction;    hyperspectral images classification;    multiscale;    low rank;   

DOI  :  10.3390/rs11121485

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown