Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Lanaras, Charis; Baltsavias, E.; Schindler, Konrad

doi:10.3390/rs9111196

Cited by 39 publications

(30 citation statements)

References 37 publications

(52 reference statements)

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“…The value of CC ranges from -1 to 1. RMSE is applied for the evaluation of reconstruction accuracy by measuring the root mean square error between the recovered HR HSI and the ground truth [47]. The smaller the RMSE value, the better reconstruction performance.…”

Section: Experiments Resultsmentioning

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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Section: Experiments Resultsmentioning

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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“…The CAVE dataset [15,39] contains 32 HSIs with a size of 512 × 512 × 31, which means that the size of each band image is 512 × 512 and the number of spectral bands is 31. Similar to [28], the high-spatial-resolution and low-spectral-resolution MSI was created by using the Nikon D700 spectral response (https://www.maxmax.com/spectral_response.htm), i.e., the spectral response matrix H H H in Equation (2).…”

Section: Hsi Datasetsmentioning

confidence: 99%

Domain Transfer Learning for Hyperspectral Image Super-Resolution

Zhang

You

2019

Remote Sensing

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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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“…Zou et al [29] proposed a double regularization HSI super-resolution by introducing spatial structure information and the nonnegative factorization. Lanaras et al [38] proposed a hyperspectral super-resolution method by jointly unmixing the two input images into pure reflectance spectra of the observed materials. Veganzones et al [27] proposed a local dictionary learning method to exploit the low rank property for HSI super-resolution.…”

Section: Related Workmentioning

confidence: 99%

Self-Dictionary Regression for Hyperspectral Image Super-Resolution

Gao

2018

Remote Sensing

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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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Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Cited by 39 publications

References 37 publications

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

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

Domain Transfer Learning for Hyperspectral Image Super-Resolution

Self-Dictionary Regression for Hyperspectral Image Super-Resolution

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