scholarly journals Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

2021 ◽  
Vol 13 (7) ◽  
pp. 1260
Author(s):  
Wenjing Chen ◽  
Xiangtao Zheng ◽  
Xiaoqiang Lu
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Fine Tuning ◽  
Spectral Mapping ◽  
Residual Network ◽  
Convolutional Networks ◽  
Supervised Methods ◽  
Image Super Resolution ◽  
Tuning Strategy

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.

scholarly journals Self-Dictionary Regression for Hyperspectral Image Super-Resolution

2018 ◽  
Vol 10 (10) ◽  
pp. 1574 ◽  
Author(s):  
Dongsheng Gao ◽  
Zhentao Hu ◽  
Renzhen Ye
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Cross Correlation ◽  
Hyperspectral Image ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

scholarly journals Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double l1 Constraint

2019 ◽  
Vol 11 (23) ◽  
pp. 2809 ◽  
Author(s):  
Tang ◽  
Xu ◽  
Huang ◽  
Huang ◽  
Sun
Keyword(s):  
Sparse Representation ◽  
Spatial Resolution ◽  
Quality Evaluation ◽  
Hyperspectral Image ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Training Data ◽  
Evaluation Indexes ◽  
Image Super Resolution ◽  
The Relationship

Hyperspectral image (HSI) super-resolution (SR) is an important technique for improving the spatial resolution of HSI. Recently, a method based on sparse representation improved the performance of HSI SR significantly. However, the spectral dictionary was learned under a fixed size, empirically, without considering the training data. Moreover, most of the existing methods fail to explore the relationship among the sparse coefficients. To address these crucial issues, an effective method for HSI SR is proposed in this paper. First, a spectral dictionary is learned, which can adaptively estimate a suitable size according to the input HSI without any prior information. Then, the proposed method exploits the nonlocal correlation of the sparse coefficients. Doubleregularized sparse representation is then introduced to achieve better reconstructions for HSI SR. Finally, a high spatial resolution HSI is generated by the obtained coefficients matrix and the learned adaptive size spectral dictionary. To evaluate the performance of the proposed method, we conduct experiments on two famous datasets. The experimental results demonstrate that it can outperform some relatively state-of-the-art methods in terms of the popular universal quality evaluation indexes.

scholarly journals Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

2019 ◽  
Vol 11 (10) ◽  
pp. 1229 ◽  
Author(s):  
Jing Hu ◽  
Minghua Zhao ◽  
Yunsong Li
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Input Image ◽  
High Spectral Resolution ◽  
Spectral Information ◽  
Spectral Correlation ◽  
Fusion Operation ◽  
Image Super Resolution ◽  
Sr Method

Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method.

scholarly journals Domain Transfer Learning for Hyperspectral Image Super-Resolution

2019 ◽  
Vol 11 (6) ◽  
pp. 694 ◽  
Author(s):  
Xiaoyan Li ◽  
Lefei Zhang ◽  
Jane You
Keyword(s):  
High Resolution ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution ◽  
Domain Transfer ◽  
Domain Transfer Learning

A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods.

Kernel-attended residual network for single image super-resolution

2021 ◽  
Vol 213 ◽  
pp. 106663
Author(s):  
Yujie Dun ◽  
Zongyang Da ◽  
Shuai Yang ◽  
Yao Xue ◽  
Xueming Qian
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution
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