Hyperspectral Image Super-Resolution Method Based on Spectral Smoothing Prior and Tensor Tubal Row-Sparse Representation

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 9; p. 2142
• Main Authors: Sun, Le, Cheng, Qihao, Chen, Zhiguo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Clustering; Datasets; Decomposition; Deep learning; Dictionaries; hyperspectral image fusion; Hyperspectral imaging; Image resolution; Mathematical analysis; Neural networks; Remote sensing; Representations; Similarity; Smoothing; Sparsity; Spatial discrimination; Spatial resolution; Spectral resolution; super-resolution; tensor decomposition; tensor sparse representation; Tensors; Wavelet transforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14092142

Due to the limited hardware conditions, hyperspectral image (HSI) has a low spatial resolution, while multispectral image (MSI) can gain higher spatial resolution. Therefore, derived from the idea of fusion, we reconstructed HSI with high spatial resolution and spectral resolution from HSI and MSI and put forward an HSI Super-Resolution model based on Spectral Smoothing prior and Tensor tubal row-sparse representation, termed SSTSR. Foremost, nonlocal priors are applied to refine the super-resolution task into reconstructing each nonlocal clustering tensor. Then per nonlocal cluster tensor is decomposed into two sub tensors under the tensor t-prodcut framework, one sub-tensor is called tersor dictionary and the other is called tensor coefficient. Meanwhile, in the process of dictionary learning and sparse coding, spectral smoothing constraint is imposed on the tensor dictionary, and L1,1,2 norm based tubal row-sparse regularizer is enforced on the tensor coefficient to enhance the structured sparsity. With this model, the spatial similarity and spectral similarity of the nonlocal cluster tensor are fully utilized. Finally, the alternating direction method of multipliers (ADMM) was employed to optimize the solution of our method. Experiments on three simulated datasets and one real dataset show that our approach is superior to many advanced HSI super-resolution methods.