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hyperspectral image Super-Resolution Meets Deep Learning:A Survey and Perspective

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1668-1691页

作者： Xinya Wang Qian Hu Yingsong Cheng Jiayi Ma the Electronic Information School Wuhan UniversityWuhan 430072China IEEE

hyperspectral image super-resolution,which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation,aims to improve the spatial resolution of the hyperspectral image,which is beneficial for subsequent applications.The development of deep learning has promoted significant progress in hyperspectral image super-resolution,and the powerful expression capabilities of deep neural networks make the predicted results more reliable.Recently,several latest deep learning technologies have made the hyperspectral image super-resolution method explode.However,a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent.To this end,in this survey,we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information.Then,we review the learning-based methods in three categories,including single hyperspectral image super-resolution,panchromatic-based hyperspectral image super-resolution,and multispectral-based hyperspectral image super-resolution.Subsequently,we summarize the commonly used hyperspectral dataset,and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively.Moreover,we briefly introduce several typical applications of hyperspectral image super-resolution,including ground object classification,urban change detection,and ecosystem monitoring.Finally,we provide the conclusion and challenges in existing learning-based methods,looking forward to potential future research directions.

关键词： Deep learning hyperspectral image image fusion image super-resolution survey

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hyperspectral image Super-Resolution Based on Spatial-Spectral Feature Extraction Network

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Chinese Journal of Electronics 2023年第3期32卷 415-428页

作者： LI Yanshan CHEN Shifu LUO Wenhan ZHOU Li XIE Weixin ATR National Key Laboratory of Defense Technology Shenzhen University Tencent

Constrained by the physics of hyperspectral sensors, the spatial resolution of hyperspectral images(HSI) is low. hyperspectral image super-resolution(HSI SR) is a task to obtain high-resolution hyperspectral images from low-resolution hyperspectral images. Existing algorithms have the problem of losing important spectral information while improving spatial resolution.To handle this problem, a spatial-spectral feature extraction network(SSFEN) for HSI SR is proposed in this paper. It enhances the spatial resolution of the HSI while preserving the spectral information. The SSFEN is composed of three parts: spatial-spectral mapping network,spatial reconstruction network, and spatial-spectral fusing network. And a joint loss function with spatial and spectral constraints is designed to guide the training of the SSFEN. Experiment results show that the proposed method improves the spatial resolution of the HSI and effectively preserves the spectral information simultaneously.

关键词： hyperspectral image Super-resolution Spectral difference Spatial-spectral feature extraction

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hyperspectral image Sharpening Based on Deep Convolutional Neural Network and Spatial-Spectral Spread Transform Models

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Journal of Donghua University(English Edition) 2023年第1期40卷 88-95页

作者：陆小辰刘晓慧杨德政赵萍阳云龙 College of Information Science and Technology Donghua UniversityShanghai 201620China

In order to improve the spatial resolution of hyperspectral(HS)image and minimize the spectral distortion,an HS and multispectral(MS)image fusion approach based on convolutional neural network(CNN)is proposed.The proposed approach incorporates the linear spectral mixture model and spatial-spectral spread transform model into the learning phase of network,aiming to fully exploit the spatial-spectral information of HS and MS images,and improve the spectral fidelity of fusion images.Experiments on two real remote sensing data under different resolutions demonstrate that compared with some state-of-the-art HS and MS image fusion methods,the proposed approach achieves superior spectral fidelities and lower fusion errors.

关键词： convolutional neural network(CNN) hyperspectral image image fusion multispectral image unmixing method

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hyperspectral image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan

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Chinese Journal of Electronics 2020年第4期29卷 608-614页

作者： WANG Xuesong LI Yiran CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology Xuzhou Key Laboratory of Artificial Intelligence and Big Data

Aiming at the difficulty of obtaining sufficient labeled hyperspectral image(HSI) data and the inconsistent feature distribution of different HSIs, a novel Unsupervised heterogeneous domain adaptation CycleGan(UHDAC) is proposed by using CycleGan to capture the transferable features in the absence of similar data.On the one hand, the two-way mapping is used to find the internal relationship between the source and target domain data,while the two-way adversary is used to constrain the source and target domain features, realizing the alignment of feature distributions.On the other hand, the CORAL loss function is introduced to minimize the distance between the second-order statistical difference between the source and target domain features, so as to solve the insufficient constraint of mapping relationship caused by the low consistency of HSI data structure in different domains.Experiments on three real HSI datasets show that UHDAC can effectively realize the unsupervised classification of target domain HSI with high classification accuracy by using the labeled HSI data in the source domain.

关键词： Unsupervised Heterogeneous Domain adaptation CycleGan hyperspectral image Classification

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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

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Science China(Information Sciences) 2023年第4期66卷 33-63页

作者： Na LIU Wei LI Yinjian WANG Ran TAO Qian DU Jocelyn CHANUSSOT School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Department of Electrical and Computer Engineering Mississippi State University GIPSA-Lab University of Grenoble Alpes Aerospace Information Research Institute Chinese Academy of Sciences

The ability to capture fine spectral discriminative information enables hyperspectral images(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors’ hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines,cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation(LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation(LRMA),LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion,destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

关键词： hyperspectral image image restoration low-rank tensor approximation multisource fusion remote sensing

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MDvT: introducing mobile three-dimensional convolution to a vision transformer for hyperspectral image classification

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International Journal of Digital Earth 2023年第1期16卷 1469-1490页

作者： Xinyao Zhou Wenzuo Zhou Xiaoli Fu Yichen Hu and Jinlian Liu of Geographical Sciences Southwest UniversityChongqingPeople's Republic of China

hyperspectral images carry numerous spectral bands,and their wealth of band data is a valuable source of information for the accurate classification of ground objects.Three-dimensional(3D)convolution,although an excellent spectral information extraction method,is limited by its huge number of parameters and long model training time.To allow better integration of 3D convolution with the most popular transformer models currently available,a new architecture called mobile 3D convolutional vision transformer(MDvT)is proposed.The MDvT introduces inverted residual structure to reduce the number of model parameters and balance the data mining efficiency of low-dimensional data input.Simultaneously,a square patch is used to cut the sequence of tokens to accelerate the model operation.Through extensive experiments,we evaluated the classification overall performance of the proposed MDvT on the WHU-Hi and Pavia University datasets,and demonstrated significant improvements in classification accuracy and model runtime compared with classical deep learning models.It is worth noting that compared with directly integrating 3D convolution into the transformer model,the MDvT architecture improves the accuracy while reducing the time to train an epoch by approximately 58.54%.To facilitate the reproduction of the work in this paper,the model code is available at https://***/gloryofroad/MDvT.

关键词： hyperspectral image Classification Convolutional neural network Transformer

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Three-Stages hyperspectral image Compression Sensing with Band Selection

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Computer Modeling in Engineering & Sciences 2023年第1期131卷 293-316页

作者： Jingbo Zhang Yanjun Zhang Xingjuan Cai Liping Xie Taiyuan University of Science and Technology Taiyuan030024China

Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effectively alleviating the bandwidth pressure during data transmission.However,CS has many shortcomings in the transmission of hyperspectral image(HSI)data.This work aims to consider the application of CS in the transmission of hyperspectral image(HSI)data,and provides a feasible research scheme for CS of HSI data.HSI has rich spectral information and spatial information in bands,which can reflect the physical properties of the target.Most of the hyperspectral image compressed sensing(HSICS)algorithms cannot effectively use the inter-band information of HSI,resulting in poor reconstruction effects.In this paper,A three-stage hyperspectral image compression sensing algorithm(Three-stages HSICS)is proposed to obtain intra-band and inter-band characteristics of HSI,which can improve the reconstruction accuracy of HSI.Here,we establish a multi-objective band selection(Mop-BS)model,amulti-hypothesis prediction(MHP)model and a residual sparse(ReWSR)model for HSI,and use a staged reconstruction method to restore the compressed HSI.The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS,and it performs best among all comparison algorithms.

关键词： Combinatorial optimization band selection hyperspectral image compressed sensing

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Review on graph learning for dimensionality reduction of hyperspectral image

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Geo-Spatial Information Science 2020年第1期23卷 98-106页

作者： Liangpei Zhang Fulin Luo State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina

Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.

关键词： hyperspectral image dimensionality reduction classification graph learning

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Dimensionality Reduction Using Optimized Self-Organized Map Technique for hyperspectral image Classification

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Computer Systems Science & Engineering 2023年第11期47卷 2481-2496页

作者： S.Srinivasan K.Rajakumar School of Computer Science and Engineering VIT UniversityVellore632014India

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

关键词： hyperspectral image dimensionality reduction depth-wise separable model water strider optimization self-organized map

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Generative adversarial networks for hyperspectral image spatial super-resolution

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The Journal of China Universities of Posts and Telecommunications 2020年第4期27卷 8-16,33页

作者： Jiang Yilin Shao Ran Tang Sanqiang College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China

It is becoming increasingly easier to obtain more abundant supplies for hyperspectral images(HSIs). Despite this, achieving high resolution is still critical. In this paper, a method named hyperspectral images super-resolution generative adversarial network(HSI-SRGAN) is proposed to enhance the spatial resolution of HSI without decreasing its spectral resolution. Different from existing methods with the same purpose, which are based on convolutional neural networks(CNNs) and driven by a pixel-level loss function, the new generative adversarial network(GAN) has a redesigned framework and a targeted loss function. Specifically, the discriminator uses the structure of the relativistic discriminator, which provides feedback on how much the generated HSI looks like the ground truth. The generator achieves more authentic details and textures by removing the place of the pooling layer and the batch normalization layer and presenting smaller filter size and two-step upsampling layers. Furthermore, the loss function is improved to specially take spectral distinctions into account to avoid artifacts and minimize potential spectral distortion, which may be introduced by neural networks. Furthermore, pre-training with the visual geometry group(VGG) network helps the entire model to initialize more easily. Benefiting from these changes, the proposed method obtains significant advantages compared to the original GAN. Experimental results also reveal that the proposed method performs better than several state-of-the-art methods.

关键词： HSI-SRGAN hyperspectral image super-resolution

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