Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery (2005.08752v2)

Abstract: Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs). However, there has been limited technical development focusing on single hyperspectral image super-resolution due to the high-dimensional and complex spectral patterns in hyperspectral image. In this paper, we make a step forward by investigating how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches for computationally efficient single hyperspectral image super-resolution, referred as SSPSR. Specifically, we introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Considering that the hyperspectral training samples are scarce and the spectral dimension of hyperspectral image data is very high, it is nontrivial to train a stable and effective deep network. Therefore, a group convolution (with shared network parameters) and progressive upsampling framework is proposed. This will not only alleviate the difficulty in feature extraction due to high-dimension of the hyperspectral data, but also make the training process more stable. To exploit the spatial and spectral prior, we design a spatial-spectral block (SSB), which consists of a spatial residual module and a spectral attention residual module. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images, and outperforms state-of-the-arts. The source code is available at \url{https://github.com/junjun-jiang/SSPSR

• The paper presents SSPN that integrates spatial residual and spectral attention modules to effectively capture hyperspectral details.
• It employs group convolution and progressive upsampling to ensure stable training even with limited hyperspectral data.
• Experimental results show SSPN outperforms state-of-the-art methods in PSNR, SSIM, and SAM metrics across diverse datasets.

Overview of "Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery"

The paper "Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery" proposes a novel deep learning approach aimed at enhancing the resolution of hyperspectral images. The authors introduce a Spatial-Spectral Prior Network (SSPN) that utilizes spatial information and spectral correlations to address the challenges inherent in hyperspectral data super-resolution tasks. This methodology is significant given the limited availability of hyperspectral training samples and the high dimensionality of spectral bands, which complicate the training of effective deep learning models.

Methodology and Contributions

The central contribution of this work is the SSPN, which features a sophisticated design to exploit spatial-spectral characteristics. Specifically, SSPN incorporates spatial residual modules and spectral attention residual modules within the Spatial-Spectral Blocks (SSBs). These modules are adept at capturing spatial information and enhancing spectral correlations, thus enabling efficient feature extraction. The authors utilize group convolution and progressive upsampling within a framework that stabilizes training and minimizes computational complexity, even under conditions of scarce training data.

A distinct aspect of the method is its conservative model parameterization through shared network parameters, which facilitates stable training results. The framework also supports flexible information flow via residual structures with short, long, and global skip connections. The paper demonstrates the model's ability to outperform existing methodologies in terms of detail recovery and numerical performance metrics across various public hyperspectral datasets.

Results and Evaluation

Experimental benchmarks conducted on hyperspectral images from urban and natural scenes (including Chikusei dataset, Pavia Centre dataset, and CAVE dataset) affirm the model's effectiveness. The SSPN achieved notable improvements in peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), spectral angle mapper (SAM), and other performance metrics compared to established approaches like VDSR, EDSR, RCAN, and various hyperspectral super-resolution models. The detailed error analysis demonstrates the advantages of SSPN over other contemporary methods relating to spatial precision and spectral fidelity.

Implications and Future Directions

The approach illustrates robust potential in practical applications where reliable high-resolution hyperspectral imaging is crucial, such as environmental monitoring, agricultural analysis, and urban planning. The integration of spatial and spectral features in a unified framework addresses hyperspectral imaging’s unique challenges, setting a precedent for future explorations in this domain.

Future research might consider expanding the SSPN’s scope to incorporate heterogeneity in hyperspectral sensors and varying imaging conditions, potentially through transfer learning or domain adaptation strategies. Additionally, enhancing computational efficiency using novel architecture optimizations or hardware acceleration could benefit real-time applications and broader adoption in field-based work.

In conclusion, the paper contributes a methodologically coherent and practically impactful model that not only advances hyperspectral image resolution technology but also hints at further possibilities for improving deep learning techniques in this specialized field.