Spatial-Spectral Manifold Embedding of Hyperspectral Data
German Aerospace Center (DLR)（德国航空航天中心）；Univ. Grenoble Alpes（格勒诺布尔大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Xi’an Jiaotong University（西安交通大学）；Beijing Institute of Technology (BIT)（北京理工大学）；INRIA（法国国家信息与自动化研究所）
In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatial-spectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods.
Remote Sensing Technology Institute（遥感技术研究所）；Helmholtz Association（亥姆霍兹联合会）；German Research Foundation (DFG)（德国研究基金会（DFG））；European Research Council (ERC)（欧洲研究委员会（ERC））；German Federal Ministry of Education and Research (BMBF)（德国联邦教育和研究部（BMBF））；AXA Research Fund（安盛研究基金）
Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination). To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semi-supervised hyperspectral dimensionality reduction (HDR), called joint and progressive subspace analysis (JPSA). The JPSA learns a high-level, semantically meaningful, jo…
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；RIKEN（理化学研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）
In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community --- can a limited amount of highly-discrimin-ative (e.g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e.g., multispectral) data? Traditional semi-supervised manifold alignment methods do not perform sufficiently well for such problems, since the hyperspectral data is very expensive to be largely collected in a trade-off between time and efficiency, compared to the multispectral data. To …
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images
School of Software Engineering, Southeast University（东南大学软件学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及交叉应用关键实验室（东南大学））；Department of Computing and Information Sciences, Saint Francis University（圣弗朗西斯大学计算机与信息科学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
National University of Defense Technology（国防科技大学）；University of Trento（特伦托大学）
This paper presents a tensor alignment (TA) based domain adaptation method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels and tensors of both domains are constructed to include neighboring samples from single superpixel. Then we consider the subspace invariance between two domains as projection matrices and original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying Tucker decomposition. To preserve geometric information in original tensors, we employ a manifold regu…
SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping
Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University（空间-时间大数据分析与应用国家级重点实验室（自然资源部），东华师范大学）；School of Geospatial Artificial Intelligence, East China Normal University（地理信息人工智能学院，东华师范大学）；Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University（地理信息科学重点实验室（教育部），东华师范大学）；School of Geographic Sciences, East China Normal University（地理科学学院，东华师范大学）；Shanghai Municipal Institute of Surveying and Mapping（上海市测绘院）；Institute of Defense Engineering, AMS（工程院，AMS）
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
Defense Innovation Institute, Chinese Academy of Military Sciences（中国人民解放军军事科学院国防科技创新研究院）；Information Research Center of Military Science, Chinese Academy of Military Sciences（中国人民解放军军事科学院军事科学信息研究中心）；College of Electrical Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）
SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
Chair of Data Science in Earth Observation（地球观测数据科学主任）；Technical University of Munich（慕尼黑技术大学）；Remote Sensing Technology Institute（遥感技术研究所）；German Aerospace Center (DLR)（德国航空航天中心）；Department of Aerospace Engineering（航空航天工程系）；University of the Bundeswehr Munich（联邦国防军慕尼黑大学）；LEAP；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化技术研究院）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔INP）；LJK
We present a new and effective approach for Hyperspectral Image (HSI) classification and clutter detection, overcoming a few long-standing challenges presented by HSI data characteristics. Residing in a high-dimensional spectral attribute space, HSI data samples are known to be strongly correlated in their spectral signatures, exhibit nonlinear structure due to several physical laws, and contain uncertainty and noise from multiple sources. In the presented approach, we generate an adaptive, structurally enriched representation environment, and employ the locally linear embedding (LLE) in it. T…
High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification
Institute of Remote Sensing and Geographic Information System, Peking University（遥感与地理信息系统研究所，北京大学）；Beijing Key Laboratory of Spatio-temporal Perception and Urban Resilience, Peking University（北京时空感知与城市韧性重点实验室，北京大学）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；Institute of energy, Peking University（北京大学能源研究所）；School of Mechanics and Engineering Science, Peking University（北京大学力学与工程科学学院）；School of Automation, Central South University（中南大学自动化学院）
Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images,…
National Disaster Reduction Center of China（国家减灾中心）；Tsinghua University（清华大学）；Institute of Applied Mathematics, AMSS, Chinese Academy of Sciences（中国科学院数学与系统科学研究院应用数学研究所）
Manifold learning is a hot research topic in the field of computer science. A crucial issue with current manifold learning methods is that they lack a natural quantitative measure to assess the quality of learned embeddings, which greatly limits their applications to real-world problems. In this paper, a new embedding quality assessment method for manifold learning, named as Normalization Independent Embedding Quality Assessment (NIEQA), is proposed. Compared with current assessment methods which are limited to isometric embeddings, the NIEQA method has a much larger application range due to t…
We present a scalable low dimensional manifold model for the reconstruction of noisy and incomplete hyperspectral images. The model is based on the observation that the spatial-spectral blocks of a hyperspectral image typically lie close to a collection of low dimensional manifolds. To emphasize this, the dimension of the manifold is directly used as a regularizer in a variational functional, which is solved efficiently by alternating direction of minimization and weighted nonlocal Laplacian. Unlike general 3D images, the same similarity matrix can be shared across all spectral bands for a hyp…
National Innovation Institute of Defense Technology, Chinese Academy of Military Science（中国军事科学院国防科技创新研究院）；National University of Defense Technology（国防科技大学）
Deep learning methods have played a more and more important role in hyperspectral image classification. However, the general deep learning methods mainly take advantage of the information of sample itself or the pairwise information between samples while ignore the intrinsic data structure within the whole data. To tackle this problem, this work develops a novel deep manifold embedding method(DMEM) for hyperspectral image classification. First, each class in the image is modelled as a specific nonlinear manifold and the geodesic distance is used to measure the correlation between the samples. …
We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points on the Grassmannian have representations as orthonormal matrices and as such do not reside in Euclidean space in the usual sense. There are a variety of metrics which allow us to determine a distance matrices that can be used to realize the Grassmannian as an embedding in Euclidean space. We illustrate that we can achieve an approximately isometric…
We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile-quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding the manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of other dimension…
Zhejiang University（浙江大学）；University of Macau（澳门大学）
Displaying the large number of bands in a hyper- spectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI but also …
EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
Department of Computer Science, National University of Computer and Emerging Sciences（计算机科学系，国立计算机与新兴科学大学）；Institute of Software Development and Engineering, Innopolis University（软件开发与工程研究所，因诺波利斯大学）；Dipartimento di Matematica e Informatica—MIFT, University of Messina（数学与信息学系—MIFT，墨西拿大学）
Istanbul Technical University（伊斯坦布尔技术大学）；Universitat de València（瓦伦西亚大学）
Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as to encapsulate the high dimensionality in a reduced set of informative features for subsequent classification, regression, or unmixing. Manifold learning methods have shown excellent performance to deal with hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high-dimension…
Huawei Helsinki Research Center, Finland（华为赫尔辛基研究中心（芬兰））；Huawei Technologies, China（华为技术有限公司（中国））
Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous data distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper pres…
Chongqing University（重庆大学）；University of Rhode Island（罗德岛大学）；Wuhan University（武汉大学）
The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm termed spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consi…
Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology (NJUST)（南京理工大学计算机科学与工程学院）；Geological Exploration Technology Institute of Jiangsu Province（江苏省地质勘查技术院）；Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology（江苏省机载探测与智能感知技术工程研究中心）
Wilkes University（威尔克斯大学）；Colorado State University（科罗拉多州立大学）；Macalester College（马卡莱斯特学院）
The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in large data sets. In this paper we apply persistent homology to the characterization of gas plumes in time dependent sequences of hyperspectral cubes, i.e. the analysis of 4-way arrays. We investigate hyperspectral movies of Long-Wavelength Infrared data monitoring an experimental release of chemical simul…
Mercedes-Benz Research and Development India Pvt. Ltd.（梅赛德斯-奔驰印度私人有限公司研发中心）；Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay（印度理工学院孟买分校资源工程研究中心）
Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational time and power, various dimensionality reduction techniques have been used for feature reduction. Some popular techniques among these falter when applied to Hyperspectral Imagery due to the famed curse of dimensionality. In this paper, we propose a novel approach, LEt-SNE, which combines graph based algorithms like t-SNE and Laplacian Eigenmaps…
City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Huaqiao University（华侨大学）；Xidian University（西安电子科技大学）；University of Macau（澳门大学）
In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatial-spectral information of HS images efficiently and effectively. Specifically, in contrast to existing methods adopting empirically-designed network modules, we formulate HS embedding as an approximation of the posterior distribution of a set of carefully-defined HS embedding events, including layer-wise spatial-spectral feature extraction and network-level feature aggregation. Then, we incorporate the proposed featu…
MINES ParisTech, PSL-Research University（巴黎矿业学院，PSL研究大学）；University of Oxford（牛津大学）
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions -- by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate each pixel to an empirical distribution of its neighbouring pixels, a judicious representation of which in an RKHS, in conjunction with the spectral information contained in the pixel itself, gi…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Sec…
Graph embedding techniques are useful to characterize spectral signature relations for hyperspectral images. However, such images consists of disjoint classes due to spatial details that are often ignored by existing graph computing tools. Robust parameter estimation is a challenge for kernel functions that compute such graphs. Finding a corresponding high quality coordinate system to map signature relations remains an open research question. We answer positively on these challenges by first proposing a kernel function of spatial and spectral information in computing neighborhood graphs. Secon…
Cosine-Normalized Attention for Hyperspectral Image Classification
SDAIA-KFUPM, Joint Research Center for Artificial Intelligence (JRCAI), King Fahd University of Petroleum and Minerals（SDAIA-KFUPM联合人工智能研究中心（JRCAI），法赫德国王石油矿产大学）；Institute of Software Development and Engineering, Innopolis University（软件开发和工程学院，因诺波利斯大学）
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
Hyperspectral images (HSI) contain a wealth of information over hundreds of contiguous spectral bands, making it possible to classify materials through subtle spectral discrepancies. However, the classification of this rich spectral information is accompanied by the challenges like high dimensionality, singularity, limited training samples, lack of labeled data samples, heteroscedasticity and nonlinearity. To address these challenges, we propose a semi-supervised graph based dimensionality reduction method named `semi-supervised spatial spectral regularized manifold local scaling cut' (S3RMLSC…
Anomaly detection (AD) has been an active research area in various domains. Yet, the increasing data scale, complexity, and dimension turn the traditional methods into challenging. Recently, the deep generative model, such as the variational autoencoder (VAE), has sparked a renewed interest in the AD problem. However, the probability distribution divergence used as the regularization is too strong, which causes the model cannot capture the manifold of the true data. In this paper, we propose the Projected Sliced Wasserstein (PSW) autoencoder-based anomaly detection method. Rooted in the optima…
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；RIKEN（理化学研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）；University of Iceland（冰岛大学）
With a large amount of open satellite multispectral imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global multispectral land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to multispectral ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace…
Hyperspectral images (HSI) not only have a broad macroscopic field of view but also contain rich spectral information, and the types of surface objects can be identified through spectral information, which is one of the main applications in hyperspectral image related research.In recent years, more and more deep learning methods have been proposed, among which convolutional neural networks (CNN) are the most influential. However, CNN-based methods are difficult to capture long-range dependencies, and also require a large amount of labeled data for model training.Besides, most of the self-super…
Self-supervised Learning for Hyperspectral Images of Trees
1 Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA 2 School of Sustainable Engineering；Built Environment, Arizona State University, Tempe, AZ 85281, USA 3 Texas A\&M AgriLife Research, Texas A\&M University, El Paso, TX 79927, USA；Texas A\&M AgriLife Research Texas A\&M University；Department of Computer Science University of Texas at El Paso；1 Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA；2 School of Sustainable Engineering；Built Environment, Arizona State University, Tempe, AZ 85281, USA；3 Texas A\&M AgriLife Research, Texas A\&M University, El Paso, TX 79927, USA；Texas A\&M AgriLife Research；Texas A\&M University；Department of Computer Science；University of Texas at El Paso
Dynamic Memory Transformer for Hyperspectral Image Classification
SDAIA-KFUPM, Joint Research Center for Artificial Intelligence (JRCAI), King Fahd University of Petroleum and Minerals（SDAIA-KFUPM联合人工智能研究中心（JRCAI）、国王法赫德石油矿物大学）
HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone
Griffith University, Australia（格里菲斯大学，澳大利亚）；Wuhan University, China（武汉大学，中国）；Nanjing University of Science and Technology, China（南京理工大学，中国）；Huaiyin Normal University, China（淮阴师范学院，中国）；Massey University, New Zealand（马斯sey大学，新西兰）
BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Key Laboratory of Fractional Signals and Systems（北京分数信号与系统重点实验室）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间生智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德雷塞尔大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程系，密苏里州立大学）
Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. However, due to the difference in various modalities, aligning the sensors and embedding their information into discriminative and compact representations is challenging. In this paper, we propose a Contrastive learning based MultiModal Alignment Network (CoMMANet) to align data from different sensors into a shared and discriminative manifold where class information is preserved. The proposed architecture uses a multimodal triplet autoencoder to cluster the latent space in such a way that samples…
LGEST: Dynamic Spatial-Spectral Expert Routing for Hyperspectral Image Classification
Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学信息中心（广州））；Faculty of Geographical Science, Beijing Normal University（北京师范大学地理学部）；School of Electronic and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）
Rochester Institute of Technology（罗切斯特理工学院）；Department of Defense（美国国防部）；Florida Atlantic University（佛罗里达大西洋大学）；University of California, Davis（加州大学戴维斯分校）
In this paper we present a new dynamical systems algorithm for clustering in hyperspectral images. The main idea of the algorithm is that data points are pushed\' in the direction of increasing density and groups of pixels that end up in the same dense regions belong to the same class. This is essentially a numerical solution of the differential equation defined by the gradient of the density of data points on the data manifold. The number of classes is automated and the resulting clustering can be extremely accurate. In addition to providing a accurate clustering, this algorithm presents a ne…
University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）
Samples from intimate (non-linear) mixtures are generally modeled as being drawn from a smooth manifold. Scenarios where the data contains multiple intimate mixtures with some constituent materials in common can be thought of as manifolds which share a boundary. Two important steps in the processing of such data are (i) to identify (cluster) the different mixture-manifolds present in the data and (ii) to eliminate the non-linearities present the data by mapping each mixture-manifold into some low-dimensional euclidean space (embedding). Manifold clustering and embedding techniques appear to be…
Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace clustering has been proven to be powerful for exploiting the intrinsic relationship between data points. Despite the impressive performance in the HSI clustering, traditional subspace clustering methods often ignore the inherent structural information among data. In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering. Specifically, the framew…
Jiangnan University（江南大学）；Nanjing University of Science and Technology（南京理工大学）
This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on hand-craft priors. Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsu…
