Journal articles: 'Hyperspectral super-Resolution'

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Sun, Shasha, Wenxing Bao, Kewen Qu, Wei Feng, Xiaowu Zhang, and Xuan Ma. "Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition." Remote Sensing 15, no. 20 (October 16, 2023): 4983. http://dx.doi.org/10.3390/rs15204983.

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This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution. This algorithm seamlessly integrates graph regularization and tensor ring decomposition, presenting an innovative fusion model that effectively leverages the spatial structure and spectral information inherent in hyperspectral images. At the core of the algorithm lies an iterative optimization process embedded within the objective function. This iterative process incrementally refines latent feature representations. It incorporates spatial smoothness constraints and graph regularization terms to enhance the quality of super-resolution reconstruction and preserve image features. Specifically, low-resolution hyperspectral images (HSIs) and high-resolution multispectral images (MSIs) are obtained through spatial and spectral downsampling, which are then treated as nodes in a constructed graph, efficiently fusing spatial and spectral information. By utilizing tensor ring decomposition, HSIs and MSIs undergo feature decomposition, and the objective function is formulated to merge reconstructed results with the original images. Through a multi-stage iterative optimization procedure, the algorithm progressively enhances latent feature representations, leading to super-resolution hyperspectral image reconstruction. The algorithm’s significant achievements are demonstrated through experiments, producing sharper, more detailed high-resolution hyperspectral images (HRIs) with an improved reconstruction quality and retained spectral information. By combining the advantages of graph regularization and tensor ring decomposition, the proposed algorithm showcases substantial potential and feasibility within the domain of hyperspectral image super-resolution.

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Zhang, Jing, Renjie Zheng, Zekang Wan, Ruijing Geng, Yi Wang, Yu Yang, Xuepeng Zhang, and Yunsong Li. "Hyperspectral Image Super-Resolution Based on Feature Diversity Extraction." Remote Sensing 16, no. 3 (January 23, 2024): 436. http://dx.doi.org/10.3390/rs16030436.

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Deep learning is an important research topic in the field of image super-resolution. Problematically, the performance of existing hyperspectral image super-resolution networks is limited by feature learning for hyperspectral images. Nevertheless, the current algorithms exhibit some limitations in extracting diverse features. In this paper, we address limitations to existing hyperspectral image super-resolution networks, focusing on feature learning challenges. We introduce the Channel-Attention-Based Spatial–Spectral Feature Extraction network (CSSFENet) to enhance hyperspectral image feature diversity and optimize network loss functions. Our contributions include: (a) a convolutional neural network super-resolution algorithm incorporating diverse feature extraction to enhance the network’s diversity feature learning by elevating the matrix rank, (b) a three-dimensional (3D) feature extraction convolution module, the Channel-Attention-Based Spatial–Spectral Feature Extraction Module (CSSFEM), to boost the network’s performance in both the spatial and spectral domains, (c) a feature diversity loss function designed based on the image matrix’s singular value to maximize element independence, and (d) a spatial–spectral gradient loss function introduced based on space and spectrum gradient values to enhance the reconstructed image’s spatial–spectral smoothness. In contrast to existing hyperspectral super-resolution algorithms, we used four evaluation indexes, PSNR, mPSNR, SSIM, and SAM, and our method showed superiority during testing with three common hyperspectral datasets.

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Zhang, Yan, Lifu Zhang, Ruoxi Song, and Qingxi Tong. "A General Deep Learning Point–Surface Fusion Framework for RGB Image Super-Resolution." Remote Sensing 16, no. 1 (December 28, 2023): 139. http://dx.doi.org/10.3390/rs16010139.

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Hyperspectral images are usually acquired in a scanning-based way, which can cause inconvenience in some situations. In these cases, RGB image spectral super-resolution technology emerges as an alternative. However, current mainstream spectral super-resolution methods aim to generate continuous spectral information at a very narrow range, limited to the visible light range. Some researchers introduce hyperspectral images as auxiliary data. But it is usually required that the auxiliary hyperspectral images have the same spatial range as RGB images. To address this issue, a general point–surface data fusion method is designed to achieve the RGB image spectral super-resolution goal in this paper, named GRSS-Net. The proposed method utilizes hyperspectral point data as auxiliary data to provide spectral reference information. Thus, the spectral super-resolution can extend the spectral reconstruction range according to spectral data. The proposed method utilizes compressed sensing theory as a fundamental physical mechanism and then unfolds the traditional hyperspectral image reconstruction optimization problem into a deep network. Finally, a high-spatial-resolution hyperspectral image can be obtained. Thus, the proposed method combines the non-linear feature extraction ability of deep learning and the interpretability of traditional physical models simultaneously. A series of experiments demonstrates that the proposed method can effectively reconstruct spectral information in RGB images. Meanwhile, the proposed method provides a framework of spectral super-resolution for different applications.

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Chang, Pai-Chuan, Jhao-Ting Lin, Chia-Hsiang Lin, Po-Wei Tang, and Yangrui Liu. "Optimization-Based Hyperspectral Spatiotemporal Super-Resolution." IEEE Access 10 (2022): 37477–94. http://dx.doi.org/10.1109/access.2022.3163266.

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Mianji, Fereidoun A., Ye Zhang, Humayun Karim Sulehria, Asad Babakhani, and Mohammad Reza Kardan. "Super-Resolution Challenges in Hyperspectral Imagery." Information Technology Journal 7, no. 7 (September 15, 2008): 1030–36. http://dx.doi.org/10.3923/itj.2008.1030.1036.

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Akgun, T., Y. Altunbasak, and R. M. Mersereau. "Super-resolution reconstruction of hyperspectral images." IEEE Transactions on Image Processing 14, no. 11 (November 2005): 1860–75. http://dx.doi.org/10.1109/tip.2005.854479.

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Bu, Lijing, Dong Dai, Zhengpeng Zhang, Yin Yang, and Mingjun Deng. "Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation." Remote Sensing 15, no. 13 (June 21, 2023): 3225. http://dx.doi.org/10.3390/rs15133225.

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Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.

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He, Zhi, and Lin Liu. "Hyperspectral Image Super-Resolution Inspired by Deep Laplacian Pyramid Network." Remote Sensing 10, no. 12 (December 2, 2018): 1939. http://dx.doi.org/10.3390/rs10121939.

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Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without fully considering the spectral information. In this paper, we propose an HSI super-resolution method inspired by the deep Laplacian pyramid network (LPN). First, the spatial resolution is enhanced by an LPN, which can exploit the knowledge from natural images without using any auxiliary observations. The LPN progressively reconstructs the high-spatial-resolution images in a coarse-to-fine fashion by using multiple pyramid levels. Second, spectral characteristics between the low- and high-resolution HSIs are studied by the non-negative dictionary learning (NDL), which is proposed to learn the common dictionary with non-negative constraints. The super-resolution results can finally be obtained by multiplying the learned dictionary and its corresponding sparse codes. Experimental results on three hyperspectral datasets demonstrate the feasibility of the proposed method in enhancing the spatial resolution of the HSI with preserving the spectral information simultaneously.

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Cao, Meng, Wenxing Bao, and Kewen Qu. "Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition." Remote Sensing 13, no. 20 (October 14, 2021): 4116. http://dx.doi.org/10.3390/rs13204116.

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The hyperspectral image super-resolution (HSI-SR) problem aims at reconstructing the high resolution spatial–spectral information of the scene by fusing low-resolution hyperspectral images (LR-HSI) and the corresponding high-resolution multispectral image (HR-MSI). In order to effectively preserve the spatial and spectral structure of hyperspectral images, a new joint regularized low-rank tensor decomposition method (JRLTD) is proposed for HSI-SR. This model alleviates the problem that the traditional HSI-SR method, based on tensor decomposition, fails to adequately take into account the manifold structure of high-dimensional HR-HSI and is sensitive to outliers and noise. The model first operates on the hyperspectral data using the classical Tucker decomposition to transform the hyperspectral data into the form of a three-mode dictionary multiplied by the core tensor, after which the graph regularization and unidirectional total variational (TV) regularization are introduced to constrain the three-mode dictionary. In addition, we impose the l1-norm on core tensor to characterize the sparsity. While effectively preserving the spatial and spectral structures in the fused hyperspectral images, the presence of anomalous noise values in the images is reduced. In this paper, the hyperspectral image super-resolution problem is transformed into a joint regularization optimization problem based on tensor decomposition and solved by a hybrid framework between the alternating direction multiplier method (ADMM) and the proximal alternate optimization (PAO) algorithm. Experimental results conducted on two benchmark datasets and one real dataset show that JRLTD shows superior performance over state-of-the-art hyperspectral super-resolution algorithms.

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Li, Xiaoyan, Lefei Zhang, and Jane You. "Domain Transfer Learning for Hyperspectral Image Super-Resolution." Remote Sensing 11, no. 6 (March 22, 2019): 694. http://dx.doi.org/10.3390/rs11060694.

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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.

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Li, Jun, Yuanxi Peng, Tian Jiang, Longlong Zhang, and Jian Long. "Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing." Applied Sciences 10, no. 16 (August 12, 2020): 5583. http://dx.doi.org/10.3390/app10165583.

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A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. Hyperspectral super-resolution aims to fuse a low spatial resolution HSI with a conventional high spatial resolution image, producing an HSI with high resolution in both the spectral and spatial dimensions. In this paper, we propose a spatial group sparsity regularization unmixing-based method for hyperspectral super-resolution. The hyperspectral image (HSI) is pre-clustered using an improved Simple Linear Iterative Clustering (SLIC) superpixel algorithm to make full use of the spatial information. A robust sparse hyperspectral unmixing method is then used to unmix the input images. Then, the endmembers extracted from the HSI and the abundances extracted from the conventional image are fused. This ensures that the method makes full use of the spatial structure and the spectra of the images. The proposed method is compared with several related methods on public HSI data sets. The results demonstrate that the proposed method has superior performance when compared to the existing state-of-the-art.

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Dong, Weisheng, Chen Zhou, Fangfang Wu, Jinjian Wu, Guangming Shi, and Xin Li. "Model-Guided Deep Hyperspectral Image Super-Resolution." IEEE Transactions on Image Processing 30 (2021): 5754–68. http://dx.doi.org/10.1109/tip.2021.3078058.

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Lanaras, Charis, Emmanuel Baltsavias, and Konrad Schindler. "Hyperspectral Super-Resolution with Spectral Unmixing Constraints." Remote Sensing 9, no. 11 (November 21, 2017): 1196. http://dx.doi.org/10.3390/rs9111196.

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Xue Song, 薛松, 张思雨 Zhang Siyu, and 刘永峰 Liu Yongfeng. "Quality Assessment of Hyperspectral Super-Resolution Images." Laser & Optoelectronics Progress 56, no. 4 (2019): 041001. http://dx.doi.org/10.3788/lop56.041001.

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Arun, P. V., Krishna Mohan Buddhiraju, Alok Porwal, and Jocelyn Chanussot. "CNN-Based Super-Resolution of Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 9 (September 2020): 6106–21. http://dx.doi.org/10.1109/tgrs.2020.2973370.

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Hu, Jing, Xiuping Jia, Yunsong Li, Gang He, and Minghua Zhao. "Hyperspectral Image Super-Resolution via Intrafusion Network." IEEE Transactions on Geoscience and Remote Sensing 58, no. 10 (October 2020): 7459–71. http://dx.doi.org/10.1109/tgrs.2020.2982940.

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Han, Xian-Hua, Yongqing Sun, Jian Wang, Boxin Shi, Yinqiang Zheng, and Yen-Wei Chen. "Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution." Sensors 19, no. 24 (December 7, 2019): 5401. http://dx.doi.org/10.3390/s19245401.

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Hyperspectral imaging is capable of acquiring the rich spectral information of scenes and has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to hardware limitations, the existed hyper-/multi-spectral imaging devices usually cannot obtain high spatial resolution. This study aims to generate a high resolution hyperspectral image according to the available low resolution hyperspectral and high resolution RGB images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectra with a data guided sparsity constraint. The proposed method firstly learns the hyperspectral dictionary from the low resolution hyperspectral image and then transforms it into the RGB one with the camera response function, which is decided by the physical property of the RGB imaging camera. Given the RGB vector and the RGB dictionary, the sparse representation of each pixel in the high resolution image is calculated with the guidance of a sparsity map, which measures pixel material purity. The sparsity map is generated by analyzing the local content similarity of a focused pixel in the available high resolution RGB image and quantifying the spectral mixing degree motivated by the fact that the pixel spectrum of a pure material should have sparse representation of the spectral dictionary. Since the proposed method adaptively adjusts the sparsity in the spectral representation based on the local content of the available high resolution RGB image, it can produce more robust spectral representation for recovering the target high resolution hyperspectral image. Comprehensive experiments on two public hyperspectral datasets and three real remote sensing images validate that the proposed method achieves promising performances compared to the existing state-of-the-art methods.

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Gao, Dongsheng, Zhentao Hu, and Renzhen Ye. "Self-Dictionary Regression for Hyperspectral Image Super-Resolution." Remote Sensing 10, no. 10 (October 1, 2018): 1574. http://dx.doi.org/10.3390/rs10101574.

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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.

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Zhang, Xizhen, Aiwu Zhang, Mengnan Li, Lulu Liu, and Xiaoyan Kang. "Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image." Sensors 20, no. 16 (August 15, 2020): 4589. http://dx.doi.org/10.3390/s20164589.

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Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.

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Zhang, Chi, Mingjin Zhang, Yunsong Li, Xinbo Gao, and Shi Qiu. "Difference Curvature Multidimensional Network for Hyperspectral Image Super-Resolution." Remote Sensing 13, no. 17 (August 31, 2021): 3455. http://dx.doi.org/10.3390/rs13173455.

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In recent years, convolutional-neural-network-based methods have been introduced to the field of hyperspectral image super-resolution following their great success in the field of RGB image super-resolution. However, hyperspectral images appear different from RGB images in that they have high dimensionality, implying a redundancy in the high-dimensional space. Existing approaches struggle in learning the spectral correlation and spatial priors, leading to inferior performance. In this paper, we present a difference curvature multidimensional network for hyperspectral image super-resolution that exploits the spectral correlation to help improve the spatial resolution. Specifically, we introduce a multidimensional enhanced convolution (MEC) unit into the network to learn the spectral correlation through a self-attention mechanism. Meanwhile, it reduces the redundancy in the spectral dimension via a bottleneck projection to condense useful spectral features and reduce computations. To remove the unrelated information in high-dimensional space and extract the delicate texture features of a hyperspectral image, we design an additional difference curvature branch (DCB), which works as an edge indicator to fully preserve the texture information and eliminate the unwanted noise. Experiments on three publicly available datasets demonstrate that the proposed method can recover sharper images with minimal spectral distortion compared to state-of-the-art methods. PSNR/SAM is 0.3–0.5 dB/0.2–0.4 better than the second best methods.

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Sahithi, V. S., and S. Agrawal. "Sub pixel location identification using super resolved multilooking CHRIS data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 463–68. http://dx.doi.org/10.5194/isprsarchives-xl-8-463-2014.

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CHRIS /Proba is a multiviewing hyperspectral sensor that monitors the earth in five different zenith angles +55°, +36°, nadir, −36° and −55° with a spatial resolution of 17 m and within a spectral range of 400–1050 nm in mode 3. These multiviewing images are suitable for constructing a super resolved high resolution image that can reveal the mixed pixel of the hyperspectral image. In the present work, an attempt is made to find the location of various features constituted within the 17m mixed pixel of the CHRIS image using various super resolution reconstruction techniques. Four different super resolution reconstruction techniques namely interpolation, iterative back projection, projection on to convex sets (POCS) and robust super resolution were tried on the −36, nadir and +36 images to construct a super resolved high resolution 5.6 m image. The results of super resolution reconstruction were compared with the scaled nadir image and bicubic convoluted image for comparision of the spatial and spectral property preservance. A support vector machine classification of the best super resolved high resolution image was performed to analyse the location of the sub pixel features. Validation of the obtained results was performed using the spectral unmixing fraction images and the 5.6 m classified LISS IV image.

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Li, Qiang, Qi Wang, and Xuelong Li. "Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution." Remote Sensing 12, no. 10 (May 21, 2020): 1660. http://dx.doi.org/10.3390/rs12101660.

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Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.

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Liu, Ziqian, Wenbing Wang, Qing Ma, Xianming Liu, and Junjun Jiang. "Rethinking 3D-CNN in Hyperspectral Image Super-Resolution." Remote Sensing 15, no. 10 (May 15, 2023): 2574. http://dx.doi.org/10.3390/rs15102574.

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Recently, CNN-based methods for hyperspectral image super-resolution (HSISR) have achieved outstanding performance. Due to the multi-band property of hyperspectral images, 3D convolutions are natural candidates for extracting spatial–spectral correlations. However, pure 3D CNN models are rare to see, since they are generally considered to be too complex, require large amounts of data to train, and run the risk of overfitting on relatively small-scale hyperspectral datasets. In this paper, we question this common notion and propose Full 3D U-Net (F3DUN), a full 3D CNN model combined with the U-Net architecture. By introducing skip connections, the model becomes deeper and utilizes multi-scale features. Extensive experiments show that F3DUN can achieve state-of-the-art performance on HSISR tasks, indicating the effectiveness of the full 3D CNN on HSISR tasks, thanks to the carefully designed architecture. To further explore the properties of the full 3D CNN model, we develop a 3D/2D mixed model, a popular kind of model prior, called Mixed U-Net (MUN) which shares a similar architecture with F3DUN. Through analysis on F3DUN and MUN, we find that 3D convolutions give the model a larger capacity; that is, the full 3D CNN model can obtain better results than the 3D/2D mixed model with the same number of parameters when it is sufficiently trained. Moreover, experimental results show that the full 3D CNN model could achieve competitive results with the 3D/2D mixed model on a small-scale dataset, suggesting that 3D CNN is less sensitive to data scaling than what people used to believe. Extensive experiments on two benchmark datasets, CAVE and Harvard, demonstrate that our proposed F3DUN exceeds state-of-the-art HSISR methods both quantitatively and qualitatively.

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Zhao, Minghua, Jiawei Ning, Jing Hu, and Tingting Li. "Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information." Remote Sensing 13, no. 12 (June 18, 2021): 2382. http://dx.doi.org/10.3390/rs13122382.

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Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.

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Tang, Zhenjie, Qing Xu, Pengfei Wu, Zhenwei Shi, and Bin Pan. "Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery." Remote Sensing 14, no. 8 (April 18, 2022): 1944. http://dx.doi.org/10.3390/rs14081944.

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Powered by advanced deep-learning technology, multi-spectral image super-resolution methods based on convolutional neural networks have recently achieved great progress. However, the single hyperspectral image super-resolution remains a challenging problem due to the high-dimensional and complex spectral characteristics of hyperspectral data, which make it difficult for general 2D convolutional neural networks to simultaneously capture spatial and spectral prior information. To deal with this issue, we propose a novel Feedback Refined Local-Global Network (FRLGN) for the super-resolution of hyperspectral image. To be specific, we develop a new Feedback Structure and a Local-Global Spectral block to alleviate the difficulty in spatial and spectral feature extraction. The Feedback Structure can transfer the high-level information to guide the generation process of low-level features, which is achieved by a recurrent structure with finite unfoldings. Furthermore, in order to effectively use the high-level information passed back, a Local-Global Spectral block is constructed to handle the feedback connections. The Local-Global Spectral block utilizes the feedback high-level information to correct the low-level feature from local spectral bands and generates powerful high-level representations among global spectral bands. By incorporating the Feedback Structure and Local-Global Spectral block, the FRLGN can fully exploit spatial-spectral correlations among spectral bands and gradually reconstruct high-resolution hyperspectral images. Experimental results indicate that FRLGN presents advantages on three public hyperspectral datasets.

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Zheng, Ke, Lianru Gao, Danfeng Hong, Bing Zhang, and Jocelyn Chanussot. "NonRegSRNet: A Nonrigid Registration Hyperspectral Super-Resolution Network." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–16. http://dx.doi.org/10.1109/tgrs.2021.3135501.

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He, Wei, Yong Chen, Naoto Yokoya, Chao Li, and Qibin Zhao. "Hyperspectral super-resolution via coupled tensor ring factorization." Pattern Recognition 122 (February 2022): 108280. http://dx.doi.org/10.1016/j.patcog.2021.108280.

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Wei, Wei, Jiangtao Nie, Yong Li, Lei Zhang, and Yanning Zhang. "Deep Recursive Network for Hyperspectral Image Super-Resolution." IEEE Transactions on Computational Imaging 6 (2020): 1233–44. http://dx.doi.org/10.1109/tci.2020.3014451.

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Jia, Jinrang, Luyan Ji, Yongchao Zhao, and Xiurui Geng. "Hyperspectral image super-resolution with spectral–spatial network." International Journal of Remote Sensing 39, no. 22 (June 19, 2018): 7806–29. http://dx.doi.org/10.1080/01431161.2018.1471546.

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Kanatsoulis, Charilaos I., Xiao Fu, Nicholas D. Sidiropoulos, and Wing-Kin Ma. "Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach." IEEE Transactions on Signal Processing 66, no. 24 (December 15, 2018): 6503–17. http://dx.doi.org/10.1109/tsp.2018.2876362.

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Zhang, Hongyan, Liangpei Zhang, and Huanfeng Shen. "A super-resolution reconstruction algorithm for hyperspectral images." Signal Processing 92, no. 9 (September 2012): 2082–96. http://dx.doi.org/10.1016/j.sigpro.2012.01.020.

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Jiangtao, Nie, Zhang Lei, Wei Wei, Yan Qingsen, Ding Chen, Chen Guochao, and Zhang Yanning. "A survey of hyperspectral image super-resolution method." Journal of Image and Graphics 28, no. 6 (2023): 1685–97. http://dx.doi.org/10.11834/jig.230038.

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Aiello, Emanuele, Mirko Agarla, Diego Valsesia, Paolo Napoletano, Tiziano Bianchi, Enrico Magli, and Raimondo Schettini. "Synthetic Data Pretraining for Hyperspectral Image Super-Resolution." IEEE Access 12 (2024): 65024–31. http://dx.doi.org/10.1109/access.2024.3396990.

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Su, Haonan, Haiyan Jin, and Ce Sun. "Deep Pansharpening via 3D Spectral Super-Resolution Network and Discrepancy-Based Gradient Transfer." Remote Sensing 14, no. 17 (August 29, 2022): 4250. http://dx.doi.org/10.3390/rs14174250.

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High-resolution (HR) multispectral (MS) images contain sharper detail and structure compared to the ground truth high-resolution hyperspectral (HS) images. In this paper, we propose a novel supervised learning method, which considers pansharpening as the spectral super-resolution of high-resolution multispectral images and generates high-resolution hyperspectral images. The proposed method learns the spectral mapping between high-resolution multispectral images and the ground truth high-resolution hyperspectral images. To consider the spectral correlation between bands, we build a three-dimensional (3D) convolution neural network (CNN). The network consists of three parts using an encoder–decoder framework: spatial/spectral feature extraction from high-resolution multispectral images/low-resolution (LR) hyperspectral images, feature transform, and image reconstruction to generate the results. In the image reconstruction network, we design the spatial–spectral fusion (SSF) blocks to reuse the extracted spatial and spectral features in the reconstructed feature layer. Then, we develop the discrepancy-based deep hybrid gradient (DDHG) losses with the spatial–spectral gradient (SSG) loss and deep gradient transfer (DGT) loss. The spatial–spectral gradient loss and deep gradient transfer loss are developed to preserve the spatial and spectral gradients from the ground truth high-resolution hyperspectral images and high-resolution multispectral images. To overcome the spectral and spatial discrepancy between two images, we design a spectral downsampling (SD) network and a gradient consistency estimation (GCE) network for hybrid gradient losses. In the experiments, it is seen that the proposed method outperforms the state-of-the-art methods in the subjective and objective experiments in terms of the structure and spectral preservation of high-resolution hyperspectral images.

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Liu, Hongyi, Wen Jiang, Yuchen Zha, and Zhihui Wei. "Coupled Tensor Block Term Decomposition with Superpixel-Based Graph Laplacian Regularization for Hyperspectral Super-Resolution." Remote Sensing 14, no. 18 (September 9, 2022): 4520. http://dx.doi.org/10.3390/rs14184520.

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Hyperspectral image (HSI) super-resolution aims at improving the spatial resolution of HSI by fusing a high spatial resolution multispectral image (MSI). To preserve local submanifold structures in HSI super-resolution, a novel superpixel graph-based super-resolution method is proposed. Firstly, the MSI is segmented into superpixel blocks to form two-directional feature tensors, then two graphs are created using spectral–spatial distance between the unfolded feature tensors. Secondly, two graph Laplacian terms involving underlying BTD factors of high-resolution HSI are developed, which ensures the inheritance of the spatial geometric structures. Finally, by incorporating graph Laplacian priors with the coupled BTD degradation model, a HSI super-resolution model is established. Experimental results demonstrate that the proposed method achieves better fused results compared with other advanced super-resolution methods, especially on the improvement of the spatial structure.

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Wang, Sai, and Fenglei Fan. "Thangka Hyperspectral Image Super-Resolution Based on a Spatial–Spectral Integration Network." Remote Sensing 15, no. 14 (July 19, 2023): 3603. http://dx.doi.org/10.3390/rs15143603.

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Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral images (HSIs), especially for the preservation and analysis of Thangka cultural heritage. However, existing CNN-based methods encounter difficulties in effectively preserving spatial information, due to challenges such as registration errors and spectral variability. To overcome these limitations, we present a novel cross-sensor super-resolution (SR) framework that utilizes high-resolution RGBs (HR-RGBs) to enhance the spectral features in low-resolution hyperspectral images (LR-HSIs). Our approach utilizes spatial–spectral integration (SSI) blocks and spatial–spectral restoration (SSR) blocks to effectively integrate and reconstruct spatial and spectral features. Furthermore, we introduce a frequency multi-head self-attention (F-MSA) mechanism that treats high-, medium-, and low-frequency features as tokens, enabling self-attention computations across the frequency dimension. We evaluate our method on a custom dataset of ancient Thangka paintings and demonstrate its effectiveness in enhancing the spectral resolution in high-resolution hyperspectral images (HR-HSIs), while preserving the spatial characteristics of Thangka artwork with minimal information loss.

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Urbina Ortega, Carlos, Eduardo Quevedo Gutiérrez, Laura Quintana, Samuel Ortega, Himar Fabelo, Lucana Santos Falcón, and Gustavo Marrero Callico. "Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples." Sensors 23, no. 4 (February 7, 2023): 1863. http://dx.doi.org/10.3390/s23041863.

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Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.

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Zhang, Xiangrong, Zitong Liu, Xianhao Zhang, and Tianzhu Liu. "A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification." Remote Sensing 16, no. 8 (April 14, 2024): 1384. http://dx.doi.org/10.3390/rs16081384.

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Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

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Shi, Songyue, Xiaoxia Gong, Yan Mu, Kevin Finch, and Gerardo Gamez. "Geometric super-resolution on push-broom hyperspectral imaging for plasma optical emission spectroscopy." Journal of Analytical Atomic Spectrometry 33, no. 10 (2018): 1745–52. http://dx.doi.org/10.1039/c8ja00235e.

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Yao, Yunze, Jianwen Hu, Yaoting Liu, and Yushan Zhao. "Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution." Remote Sensing 15, no. 12 (June 12, 2023): 3066. http://dx.doi.org/10.3390/rs15123066.

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Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation complexity. To solve the above problems, a novel method based on a channel multilayer perceptron (CMLP) is presented in this article, which aims to obtain a better performance while reducing the computational cost. To sufficiently extract spectral features, a local-global spectral integration block is proposed, which consists of CMLP and some parameter-free operations. The block can extract local and global spectral features with low computational cost. In addition, a spatial feature group extraction block based on the CycleMLP framework is designed; it can extract local spatial features well and reduce the computation complexity and number of parameters. Extensive experiments demonstrate that our method achieves a good performance compared with other methods.

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Li, Jiaojiao, Ruxing Cui, Bo Li, Rui Song, Yunsong Li, and Qian Du. "Hyperspectral Image Super-Resolution with 1D–2D Attentional Convolutional Neural Network." Remote Sensing 11, no. 23 (December 1, 2019): 2859. http://dx.doi.org/10.3390/rs11232859.

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Hyperspectral image (HSI) super-resolution (SR) is of great application value and has attracted broad attention. The hyperspectral single image super-resolution (HSISR) task is correspondingly difficult in SR due to the unavailability of auxiliary high resolution images. To tackle this challenging task, different from the existing learning-based HSISR algorithms, in this paper we propose a novel framework, i.e., a 1D–2D attentional convolutional neural network, which employs a separation strategy to extract the spatial–spectral information and then fuse them gradually. More specifically, our network consists of two streams: a spatial one and a spectral one. The spectral one is mainly composed of the 1D convolution to encode a small change in the spectrum, while the 2D convolution, cooperating with the attention mechanism, is used in the spatial pathway to encode spatial information. Furthermore, a novel hierarchical side connection strategy is proposed for effectively fusing spectral and spatial information. Compared with the typical 3D convolutional neural network (CNN), the 1D–2D CNN is easier to train with less parameters. More importantly, our proposed framework can not only present a perfect solution for the HSISR problem, but also explore the potential in hyperspectral pansharpening. The experiments over widely used benchmarks on SISR and hyperspectral pansharpening demonstrate that the proposed method could outperform other state-of-the-art methods, both in visual quality and quantity measurements.

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CHEN, GUANGYI, SHEN-EN QIAN, JEAN-PIERRE ARDOUIN, and WENFANG XIE. "SUPER-RESOLUTION OF HYPERSPECTRAL IMAGERY USING COMPLEX RIDGELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 03 (May 2012): 1250025. http://dx.doi.org/10.1142/s0219691312500257.

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In this paper, a novel super-resolution method for hyperspectral imagery is proposed by using complex ridgelet transform. A Radon transform is first applied to each band image of a datacube to be enhanced to obtain the Radon slices, and then a 1D dual-tree complex wavelet transform is conducted along each Radon slice to generate coefficients of the complex ridgelet transform. The ordinary ridgelet transform or the finite ridgelet transform (FRIT), however, uses the 1D scalar wavelet transform instead of the dual-tree complex wavelet transform along each Radon slice. The reason why the dual-tree complex wavelet is adopted in this paper is because it has the property of approximate shift invariance, which is very important in image super-resolution. Experiments are conducted in this paper to demonstrate the advantages of the proposed method over the wavelet super-resolution, the FRIT image fusion, and the principal component analysis fusion.

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Kim, Byunghyun, and Soojin Cho. "Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization." Applied Sciences 9, no. 20 (October 19, 2019): 4444. http://dx.doi.org/10.3390/app9204444.

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In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.

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Marquez Castellanos, Miguel Angel, Cesar Augusto Vargas, and Henry Arguello. "Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging." Ingeniería e Investigación 36, no. 3 (December 19, 2016): 117. http://dx.doi.org/10.15446/ing.investig.v36n3.54267.

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Hyperspectral imaging (HSI) is used in a wide range of applications such as remote sensing, space imagery, mineral detection, and exploration. Unfortunately, it is difficult to acquire hyperspectral images with high spatial and spectral resolution due to instrument limitations. The super-resolution techniques are used to reconstruct low-resolution hyperspectral images. However, traditional superresolution (SR) approaches do not allow direct use of both spatial and spectral information, which is a decisive for an optimal reconstruction. This paper proposes a single image SR algorithm for HSI. The algorithm uses the fact that the spatial and spectral information can be integrated to make an accurate estimate of the high-resolution HSI. To achieve this, two types of spatio- pectral downsampling, and a three-dimensional interpolation are proposed in order to increase coherence between the spatial and spectral information. The resulting reconstructions using the proposed method are up to 2 dB better than traditional SR approaches.

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Liu, Zhe, Yinqiang Zheng, and Xian-Hua Han. "Deep Unsupervised Fusion Learning for Hyperspectral Image Super Resolution." Sensors 21, no. 7 (March 28, 2021): 2348. http://dx.doi.org/10.3390/s21072348.

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Hyperspectral image (HSI) super-resolution (SR) is a challenging task due to its ill-posed nature, and has attracted extensive attention by the research community. Previous methods concentrated on leveraging various hand-crafted image priors of a latent high-resolution hyperspectral (HR-HS) image to regularize the degradation model of the observed low-resolution hyperspectral (LR-HS) and HR-RGB images. Different optimization strategies for searching a plausible solution, which usually leads to a limited reconstruction performance, were also exploited. Recently, deep-learning-based methods evolved for automatically learning the abundant image priors in a latent HR-HS image. These methods have made great progress for HS image super resolution. Current deep-learning methods have faced difficulties in designing more complicated and deeper neural network architectures for boosting the performance. They also require large-scale training triplets, such as the LR-HS, HR-RGB, and their corresponding HR-HS images for neural network training. These training triplets significantly limit their applicability to real scenarios. In this work, a deep unsupervised fusion-learning framework for generating a latent HR-HS image using only the observed LR-HS and HR-RGB images without previous preparation of any other training triplets is proposed. Based on the fact that a convolutional neural network architecture is capable of capturing a large number of low-level statistics (priors) of images, the automatic learning of underlying priors of spatial structures and spectral attributes in a latent HR-HS image using only its corresponding degraded observations is promoted. Specifically, the parameter space of a generative neural network used for learning the required HR-HS image to minimize the reconstruction errors of the observations using mathematical relations between data is investigated. Moreover, special convolutional layers for approximating the degradation operations between observations and the latent HR-HS image are specifically to construct an end-to-end unsupervised learning framework for HS image super-resolution. Experiments on two benchmark HS datasets, including the CAVE and Harvard, demonstrate that the proposed method can is capable of producing very promising results, even under a large upscaling factor. Furthermore, it can outperform other unsupervised state-of-the-art methods by a large margin, and manifests its superiority and efficiency.

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Li, Xuesong, Youqiang Zhang, Zixian Ge, Guo Cao, Hao Shi, and Peng Fu. "Adaptive Nonnegative Sparse Representation for Hyperspectral Image Super-Resolution." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 4267–83. http://dx.doi.org/10.1109/jstars.2021.3072044.

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Zhang, Shaolei, Guangyuan Fu, Hongqiao Wang, and Yuqing Zhao. "Spectral recovery‐guided hyperspectral super‐resolution using transfer learning." IET Image Processing 15, no. 11 (May 20, 2021): 2656–65. http://dx.doi.org/10.1049/ipr2.12253.

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Lu, Xiaochen, Xiaohui Liu, Lei Zhang, Fengde Jia, and Yunlong Yang. "Hyperspectral image super-resolution based on attention ConvBiLSTM network." International Journal of Remote Sensing 43, no. 13 (July 3, 2022): 5059–74. http://dx.doi.org/10.1080/01431161.2022.2128701.

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Pan, Bin, Qiaoying Qu, Xia Xu, and Zhenwei Shi. "Structure–Color Preserving Network for Hyperspectral Image Super-Resolution." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–12. http://dx.doi.org/10.1109/tgrs.2021.3135028.

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Li, Qiang, Yuan Yuan, and Qi Wang. "Hyperspectral image super-resolution via multi-domain feature learning." Neurocomputing 472 (February 2022): 85–94. http://dx.doi.org/10.1016/j.neucom.2021.10.041.

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Journal articles on the topic 'Hyperspectral super-Resolution'

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