Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image

Sun, Xuejian; Zhang, Lifu; Yang, Hang; Wu, Tong; Yi, Chuixiang; Guo, Yi

doi:10.1109/jstars.2014.2356512

Cited by 52 publications

(22 citation statements)

References 28 publications

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“…In this paper, spatial resolution enhancement algorithms for comparison (PAN-sharpening algorithms) include GSA method [43], Indusion method [44], and SparseFI method [35]. Arad's method [25] and SREM method [24] are used to compare the performance of spectral resolution enhancement.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Several spectral reconstruction methods are proposed to improve spectral resolution. For example, a spectral resolution enhancement method (SREM) [24] recovers a HSI with wide swath by estimating spectral response matrix. Arad et al [25] proposed a spectral resolution enhancement algorithm based on sparse representation, where a spectral dictionary is learned from prior HSIs through K-means singular value decomposition (K-SVD), and HSI is recovered from the input RGB image.…”

Section: Spectral Resolution Enhancement Techniquesmentioning

confidence: 99%

“…Atmospheric correction and registration are firstly performed. Fast Line-of-sight Atmospheric Analysis in ENVI 5.3 is used for atmospheric correction [24]. The polynomial algorithm is exploited to register Hyperion image to ALI Figure 9a shows the PAN image, and RGB compositions of ALI and Hyperion images are presented in Figure 9b,c.…”

Section: Experiments On Real Datasetmentioning

confidence: 99%

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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%

Section: Spectral Resolution Enhancement Techniquesmentioning

confidence: 99%

Section: Experiments On Real Datasetmentioning

confidence: 99%

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

et al. 2020

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“…Here, a blind approach focusing the inherent data redundancy to achieve better results is followed. Xuejian Sun et al in [21] proposes a spectral resolution enhancement method (SREM) for remotely sensed MS image, to generate wide swath HS images using auxiliary multi/hyper-spectral data. Transformation matrices are generated followed by a spectral angle weighted minimum distance (SAWMD) matching method to create HS vectors from the original MS image, pixel by pixel.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Fusion in Remote Sensing Applications: A Review

Pandit¹,

Bhiwani²

2015

IJCA

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Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the information from combinations of panchromatic, multispectral or hyperspectral images; intended to result in a composite image having both higher spatial and higher spectral resolutions. The research in this area cites date back to last few decades, but the diverse approaches proposed so far by different researchers have been rarely discussed at one place. This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms. General TermsRemote sensing, Image fusion, Satellite image processing.

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“…Recently, a spectral resolution enhancement method (SREM) for remotely sensed MSI has been introduced using auxiliary multispectral/hyperspectral data [9]. In this method, a number of spectra of different materials is extracted from both the MSI and HSI data.…”

Section: Introductionmentioning

confidence: 99%

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

et al. 2017

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Abstract:In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR) or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently.

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Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image

Cited by 52 publications

References 28 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

Image Fusion in Remote Sensing Applications: A Review

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

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