Fusing Multiple Multiband Images

Arablouei, Reza

doi:10.3390/jimaging4100118

Cited by 12 publications

(5 citation statements)

References 80 publications

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“…The performance of the proposed algorithm is evaluated using Salinas Dataset (Hyperspectral Remote Sensing Scenes), DC Mall Dataset and Moffett Dataset (Arablouei, 2018). The proposed as well as the existing algorithms have been implemented in Matlab R2017a on the Microsoft Windows environment on a PC with processor Intel(R) Core(TM) i5-8250U CPU, 8 GB RAM and 64 bit operating system.…”

Section: Resultsmentioning

confidence: 99%

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Remote Sensing Image Fusion (RSIF) of Hyperspectral and Panchromatic Data Using Adaptive Guided Filter

Gul,

Kaur

2024

Preprint

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Purpose Hyperspectral data are the most widely used remote sensing datasets. Hyperspectral Pan-Sharpening suffers from spectral distortion; the purpose of hyperspectral image fusion is to effectively inject the missing spatial detail into the HS image, while preserving the spectral information. Edge-preserving smoothing filters such as Guided Filter retains image edge and structure details while minimizing noise, gradient reversal, undershoot and overshoot artefacts. However, it exhibits halo artefacts. Method This paper introduces an innovative algorithm for panchromatic and hyperspectral image fusion. By employing the Adaptive Guided Filter, we enhance image sharpness and mitigate halo artefacts – objectionable counter shading around edges. This preserves image structure and aesthetic quality, maintaining the speed of Guided Filter. Results We have applied our technique on three Hyperspectral datasets such as DCMall, Salinas, and Moffett. Our technique has shown visually improved results for the halo artefacts over Guided Filter. We have established a comparison of our technique with Guided Filter in terms of fusion quality metrics such as cross correlation (CC), spectral angle mapper (SAM), root mean squared error (RMSE) and Erreur relative globale adimensionnelle de synthèse (ERGAS). Conclusion At smoothness (𝛔=2), our technique has shown better results for CC, SAM, RMSE, and ERGAS, indicating Adaptive Guided Filter perform better over Guided Filter in terms of retaining spatial quality and spectral quality.

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Section: Resultsmentioning

confidence: 99%

“…). The Salinas Dataset used for the experimental purpose has been collected from [18], the Dc Mall and the Moffett dataset have been collected from[19]. The Salinas Data scene has been collected by the 224-band AVIRIS sensor over Salinas Valley,…”

mentioning

confidence: 99%

Remote Sensing Image Fusion (RSIF) of Hyperspectral and Panchromatic Data Using Adaptive Guided Filter

Gul,

Kaur

2024

Preprint

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“…In Arablouei [ 8 ], a new algorithm is proposed capable of simultaneously fusing multiple multiband HSI images. The used method relies on a forward observation model together with a linear mixture model.…”

Section: Contributionsmentioning

confidence: 99%

The Future of Hyperspectral Imaging

Selci

2019

J. Imaging

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The Special Issue on hyperspectral imaging (HSI), entitled "The Future of Hyperspectral Imaging", has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls. Keywords: hyperspectral imaging; medical imaging by HSI; HSI for biology; remote sensing; hyperspectral microscopy; fluorescence hyperspectral imaging; Raman hyperspectral imaging; infrared hyperspectral imaging; statistical methods for HSI; hyperspectral data mining and compression; statistical methods for HSI; hyperspectral data mining and compression

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“…For example, HS-PAN image fusion (also known as hyperspectral pan sharpening) [6,8,9] can be broadly divided into six classes: component substitution (CS), multiresolution analysis (MRA), Bayesian, matrix factorization, deep learning and hybrid meth-ods. For improvement of the fusion performance, jointly fusing HS, MS and PAN images has been investigated [10]. However, this method did not take into account the lowdimensional structure of multiband image, which has recently gained much interest [11,12].…”

Section: Introductionmentioning

confidence: 99%

Multiband Image Fusion via Regularization on a Riemannian Submanifold

Pan,

Jing,

Leung

et al. 2023

Remote Sensing

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Multiband image fusion aims to generate high spatial resolution hyperspectral images by combining hyperspectral, multispectral or panchromatic images. However, fusing multiband images remains a challenge due to the identifiability and tracking of the underlying subspace across varying modalities and resolutions. In this paper, an efficient multiband image fusion model is proposed to investigate the latent structures and intrinsic physical properties of a multiband image, which is characterized by the Riemannian submanifold regularization method, nonnegativity and sum-to-one constraints. An alternating minimization scheme is proposed to recover the latent structures of the subspace via the manifold alternating direction method of multipliers (MADMM). The subproblem with Riemannian submanifold regularization is tackled by the projected Riemannian trust-region method with guaranteed convergence. The effectiveness of the proposed method is demonstrated on two multiband image fusion problems: (1) hyperspectral and panchromatic image fusion and (2) hyperspectral, multispectral and panchromatic image fusion. The experimental results confirm that our method demonstrates superior fusion performance with respect to competitive state-of-the-art fusion methods.

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Fusing Multiple Multiband Images

Cited by 12 publications

References 80 publications

Remote Sensing Image Fusion (RSIF) of Hyperspectral and Panchromatic Data Using Adaptive Guided Filter

Remote Sensing Image Fusion (RSIF) of Hyperspectral and Panchromatic Data Using Adaptive Guided Filter

The Future of Hyperspectral Imaging

Multiband Image Fusion via Regularization on a Riemannian Submanifold

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