Related references
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Article
Computer Science, Artificial Intelligence
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Summary: The fusion of multispectral and hyperspectral images, aiming to combine high spatial resolution MS images with HS data showing a lower spatial resolution but a more accurate spectral resolution, has gained significant attention. This survey provides a comprehensive review of the subject and classifies the related approaches into three categories: pansharpening-based, decomposition-based, and machine learning-based. It also discusses widely used datasets, performance assessment, open issues, and future research directions.
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Summary: In this article, a novel deep neural network architecture called TDNet is proposed for pansharpening, which utilizes the spatial details of the panchromatic image to enhance the spatial resolution of the multispectral image. Experimental results demonstrate the superiority of TDNet compared to other methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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(2023)
Article
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Weiwei Sun et al.
Summary: The paper proposed a band divide-and-conquer framework (BDCF) to address the nonoverlapped spectrum range problem between low spatial resolution hyperspectral (HS) and high spatial resolution multispectral (MS) images, achieving high fidelity HR HS fused image across the entire spectrum. The framework demonstrated superior performance in spectral fidelity and spatial details, with competitive fusion behaviors compared to state-of-the-art methods, while maintaining relatively higher computational efficiency.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zhong-Cheng Wu et al.
Summary: This article proposes a generic fusion framework for pansharpening which combines variational optimization with deep learning. The framework achieves higher accuracy and generalization ability compared to recent state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jianjun Liu et al.
Summary: This article focuses on hyperspectral image (HSI) super-resolution, which aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI. The proposed model-inspired deep network is designed in an unsupervised manner and has been proven effective through experiments.
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(2022)
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Summary: In this article, an iterative regularization method based on tensor subspace representation (IR-TenSR) is proposed for hyperspectral image super-resolution (HSI-SR). A tensor subspace representation (TenSR)-based regularization model is introduced, which integrates the global spectral-spatial low-rank and the nonlocal self-similarity priors of high spatial resolution hyperspectral image (HR-HSI). An iterative regularization procedure is designed to utilize the residual information of low-resolution images, and an effective algorithm based on the proximal alternating minimization method is developed to solve the TenSR-regularization model. Experimental results demonstrate the superiority of IR-TenSR compared to state-of-the-art fusion approaches.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
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Summary: This paper proposes a new hyperspectral pansharpening network architecture (Hyper-DSNet) to fully preserve latent spatial details and spectral fidelity through a deep-shallow fusion structure, multi-detail extractor, and spectral attention.
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