Exploiting Clustering Manifold Structure for Hyperspectral Imagery Super-Resolution

Zhang, Lei; Wei, Wei; Bai, Chengcheng; Gao, Yifan; Zhang, Yanning

doi:10.1109/tip.2018.2862629

Cited by 126 publications

(59 citation statements)

References 35 publications

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“…For matrixing three-dimensional HSI and multispectral image (MSI) that are prone to inducing loss of structural information, Kanatsoulis et al [9] addressed the problem from a tensor perspective and established a coupled tensor factorization framework. Zhang et al [10] discovered that the clustering manifold structure of the latent HSI can be well preserved in the spatial domain of the input conventional image, and proposed to super-resolve the HSI by this discovery. Considering that the sparse based methods tackle each pixel independently, Han et al [11] utilized a self-similarity prior as the constraint for sparse representation of the HSI and MSI.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Zhao

2019

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Zhao

2019

Remote Sensing

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“…Input LR MSI is generated by spatial down-sampling and spectral down-sampling, respectively. A Gaussian filter and a spatial down-sampling factor are applied for spatial down-sampling [49]. For Indian pines dataset and Cuprite dataset, spectral down-sampling is implemented using the given spectral response function between Landsat TM and AVIRIS.…”

Section: Experiments On Simulated Datasetsmentioning

confidence: 99%

Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

et al. 2020

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This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial-or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial-and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial-and spectral-resolution enhancement methods.Remote Sens. 2020, 12, 993 2 of 24 remote sensing data have become increasingly available and the corresponding applications have attracted wide interests, there are existing challenges in acquiring images with simultaneously high spatial resolution and high spectral resolution [6]. Therefore, many research focuses on recovering high quality synthetic image from low resolution (LR) inputs, including spatial resolution improvement approaches [7][8][9][10][11][12][13][14][15][16][17][18][19] and spectral resolution enhancement techniques [20-27].1.1. Spatial Resolution Improvement of HSI Over past decades, several spatial improvement methods have been proposed based on the fusion of MSI and PAN images. Hyperspectral pan-sharpening improves spatial resolution of HSI by fusing LR HSI with a high resolution (HR) PAN image covering a spectral range from the visible to the near infrared ranges [7]. Except for the classical hyperspectral pan-sharpening approaches such as component substitution (CS) methods [8], multi-resolution analysis (MRA) methods [9], and model based optimization methods [10,11], deep learning, particularly convolutional neural network (CNN), has been widely exploited in pan-sharpening tasks. In [12], a residual CNN model is designed to describe the mapping between LR/HR MSI pairs and PAN image. Yuan et al. [13] proposed a multi-scale and multi-depth CNN (MSDCNN) for pan-sharpening, in which multi-scale feature extraction is used to reconstruct the HR MSI.HSI fusion is another popular approach to spatial resolution improvement. A HR HSI is recovered by fusing a LR HSI with a H...

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“…These images have been widely applied to many fields [1][2][3], such as urban planning, natural hazard detection, and environment monitoring. For this reason, more and more research efforts have been put into developing methods for remote sensing scene classification which is a hot research topic in the remote sensing field to better interpret the images [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Remote sensing scene classification based on rotation-invariant feature learning and joint decision making

Zhou

Liu

Zhao

et al. 2019

J Image Video Proc.

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With the popular use of high-resolution satellite images, remote sensing scene classification has always been a hot research topic in its related areas. However, limited to the issues of remote sensing datasets including the small scale of scene classes, the lack of rich label information and so on, it is quite challenging for deep learning methods to learn powerful feature representation. To overcome this problem, we propose a rotation-invariant feature learning and joint decision-making method based on Siamese convolutional neural networks with the combination of identification and verification models. Firstly, a novel data augmentation strategy is proposed specially for the Siamese model to learning rotation-invariant features. Secondly, a joint decision mechanism is introduced in our method, which is realized by the identification and verification model to better improve the classification performance. The proposed method can not only suppress problems caused by lack of rich label samples but also improve the robustness of Siamese convolutional neural networks. Experimental results demonstrate that the proposed method is effective and efficient for remote sensing scene classification.

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Exploiting Clustering Manifold Structure for Hyperspectral Imagery Super-Resolution

Cited by 126 publications

References 35 publications

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

Remote sensing scene classification based on rotation-invariant feature learning and joint decision making

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