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Article
Computer Science, Artificial Intelligence
Gemine Vivone
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.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Tian-Jiang Zhang et al.
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)
Article
Computer Science, Artificial Intelligence
Jie Xiao et al.
Summary: Existing deep learning approaches for image de-raining face limitations due to convolutional architectures. This paper proposes an efficient transformer-based architecture that incorporates general priors of visual tasks and design complementary window-based transformer and spatial transformer to overcome these limitations and achieve better de-raining performance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Geochemistry & Geophysics
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.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Cheng Jin et al.
Summary: This paper proposes a new Pansharpening network architecture that utilizes Laplacian pyramid method, fusion convolutional neural network, and total loss consisting of multiple across scale loss functions to achieve higher spatial resolution of multispectral images.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Qi Xie et al.
Summary: This paper presents a network architecture called MHF-net for multispectral and hyperspectral image fusion task. MHF-net not only has clear interpretability, but also effectively combines high-resolution multispectral and low-resolution hyperspectral images. With careful design of the model and algorithm, MHF-net demonstrates good generalization capability and outperforms state-of-the-art methods in both simulated and real data.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jin-Fan Hu et al.
Summary: The paper introduces a deep convolutional neural network architecture to fuse low-resolution HSI and high-resolution multispectral image for generating high-resolution HSI. By preserving spatial and spectral information using LR-HSI and HR-MSI, and utilizing attention and pixelShuffle modules for high-quality spatial details extraction, the proposed network achieves the best performance compared to recent HSI super-resolution approaches.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Geochemistry & Geophysics
Liang-Jian Deng et al.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2022)
Article
Geochemistry & Geophysics
Ting Xu et al.
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)
Article
Engineering, Electrical & Electronic
Yu-Wei Zhuo et al.
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.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jin-Fan Hu et al.
Summary: This paper proposes a Fusformer method based on the Transformer architecture to solve the hyperspectral image super-resolution problem. By utilizing self-attention mechanism, this method can globally explore the intrinsic relationship within features, achieving better performance than current state-of-the-art methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Jin-Liang Xiao et al.
Summary: In this paper, we propose a context-aware details injection fidelity method that explores the intricate relationship between PAN and HRMS images in the gradient domain for pansharpening. The proposed method achieves effective and robust results as supported by qualitative and quantitative assessments on different datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Renwei Dian et al.
Summary: This article introduces a novel HSI and MSI fusion method, combining subspace representation and CNN denoiser, trained on gray images and directly applicable to any HSI and MSI datasets for superior performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Geochemistry & Geophysics
Gemine Vivone et al.
Summary: This article introduces the concept of pansharpening and the latest advances in MS pansharpening, proposing a new benchmark for comparing different methods on various scenarios and sensors.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2021)
Article
Geochemistry & Geophysics
Danfeng Hong et al.
Summary: This study introduces a general multimodal deep learning (MDL) framework for the classification and identification challenges in geoscience and remote sensing. By investigating a special case of multi-modality learning (MML), the study presents five fusion strategies and demonstrates how to train deep networks and build network architectures effectively. Experimental results on two different multimodal RS data sets confirm the efficiency and advantages of the proposed MDL framework.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Renwei Dian et al.
Summary: This paper provides a comprehensive review and categorization of hyperspectral image (HSI) and multispectral image (MSI) fusion methods, including pan-sharpening, matrix factorization, tensor representation, and deep convolution neural network. The characteristics, discussions, and comparisons of various fusion methods are introduced, along with the existing challenges and potential future directions for HSI-MSI fusion.
INFORMATION FUSION
(2021)
Article
Mathematics, Applied
Xue-Cheng Tai et al.
Summary: This paper proposes a multiphase image segmentation method which solves the min-cut minimization problem under the multigrid method framework. By converting the min-cut problem to equivalent forms and using classical multigrid methods, the proposed method has been demonstrated to be both efficient and effective through extensive experiments on simulated and real images.
JOURNAL OF SCIENTIFIC COMPUTING
(2021)
Article
Geochemistry & Geophysics
Xueting Zhang et al.
Summary: The article proposes an interpretable spatial-spectral reconstruction network (SSR-NET) based on CNN for efficient fusion of HSI and MSI. The SSR-NET consists of three components for cross-mode message inserting, spatial reconstruction, and spectral reconstruction, achieving superior or competitive results in comparison with seven state-of-the-art methods on six HSI data sets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Liang-Jian Deng et al.
Summary: The article discusses the fusion of high spatial resolution PAN data with lower spatial resolution MS data using machine learning techniques and fusion schemes, specifically deep convolutional neural networks (DCNNs). The DCNNs are combined with traditional fusion schemes to estimate nonlinear injection models for combining upsampled low-resolution MS images with detailed information extracted from two philosophies, leading to improved results compared to other methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Jie Huang et al.
Summary: Sparse unmixing is an important technique for analyzing hyperspectral images, and this article proposes a nonlocal tensor-based sparse unmixing algorithm that groups similar patches in the image and applies low-rank constraint and joint sparsity for accurate abundance estimation. The effectiveness of the algorithm is demonstrated through experiments with simulated and real hyperspectral data sets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Yuhui Yuan et al.
Summary: In this paper, a new context aggregation scheme called object context is proposed to enhance object information in semantic segmentation tasks. The method utilizes dense relation matrix and efficient interlaced sparse self-attention to model pixel relations, and combines with conventional multi-scale context schemes for richer context information. The approach shows competitive performances on challenging benchmarks like Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Article
Computer Science, Artificial Intelligence
Weisheng Dong et al.
Summary: The trade-off between spatial and spectral resolution is a fundamental issue in hyperspectral images. Model-based image fusion methods and deep learning-based methods have been proposed for hyperspectral pan-sharpening, but there is still room for improvement. The MoG-DCN algorithm proposed in this paper outperforms leading HSISR methods in terms of both implementation cost and visual quality.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Xiangyu Liu et al.
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(2020)
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Renlong Hang et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2020)
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Automation & Control Systems
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IEEE TRANSACTIONS ON CYBERNETICS
(2020)
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Behnood Rasti et al.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2020)
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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2020)
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Geochemistry & Geophysics
Gemine Vivone et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2019)
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2019)
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Liang-Jian Deng et al.
SIAM JOURNAL ON IMAGING SCIENCES
(2019)
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IEEE TRANSACTIONS ON IMAGE PROCESSING
(2018)
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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(2017)
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(2015)
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(2015)
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Chao Dong et al.
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
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Chao Dong et al.
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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
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