Dimensionality reduction is a crucial preprocessing for hyperspectral data analysis - finding an appropriate subspace is often required for subsequent image classification. In recent work, we proposed supervised angular information based dimensionality reduction methods to find effective subspaces. Since unlabeled data are often more readily available compared to labeled data, we propose an unsupervised projection that finds a lower dimensional subspace where local angular information is preserved. To exploit spatial information from the hyperspectral images, we further extend our unsupervised projection to incorporate spatial contextual information around each pixel in the image. Additionally, we also propose a sparse representation based classifier which is optimized to exploit spatial information during classification - we hence assert that our proposed projection is particularly suitable for classifiers where local similarity and spatial context are both important. Experimental results with two real-world hyperspectral datasets demonstrate that our proposed methods provide a robust classification performance.
Khwaja Fareed University of Engineering and Information Technology（赫瓦贾·法里德工程与信息技术大学）
Traditional Active/Self/Interactive Learning for Hyperspectral Image Classification (HSIC) increases the size of the training set without considering the class scatters and randomness among the existing and new samples. Second, very limited research has been carried out on joint spectral-spatial information and finally, a minor but still worth mentioning is the stopping criteria which not being much considered by the community. Therefore, this work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving (FLG) meth…
MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models
School of Computer Science, Wuhan University（武汉大学计算机科学学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；China Petroleum Pipeline Engineering Corporation（中国石油管道局工程有限公司）；Hebei Key Laboratory of Underground Energy Storage Technology（河北省地下储能技术重点实验室）；No. 7 Oil Production Plant, Changqing Oilfield Branch, PetroChina Company Limited（中国石油天然气股份有限公司长庆油田分公司第七采油厂）；Faculty of Electrical Engineering and Computer Science, Ningbo University（宁波大学电气工程与计算机科学学院）；College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Aerospace Information, Space Engineering University（航天工程大学航天信息学院）；Key Laboratory of Intelligent Processing and Applicati（智能处理与应用重点实验室）
Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration
School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney（电气与数据工程学院，工程与信息技术学院，技术悉尼大学）；Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology（可信自主系统研究所和计算机科学与工程系，南方科技大学）
We present a novel Discriminant Locality Preserving Projections (DLPP) algorithm named Collaborative Discriminant Locality Preserving Projection (CDLPP). In our algorithm, the discriminating power of DLPP are further exploited from two aspects. On the one hand, the global optimum of class scattering is guaranteed via using the between-class scatter matrix to replace the original denominator of DLPP. On the other hand, motivated by collaborative representation, an $L_2$-norm constraint is imposed to the projections to discover the collaborations of dimensions in the sample space. We apply our a…
Chongqing University（重庆大学）；Rutgers University（罗格斯大学）；Advanced Digital Sciences Center（先进数字科学中心）
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data. In our approach, an additional constraint of the geometry of classes is imposed to the objective function of conventional LPP for respecting some more global manifold structures. Moreover, we formulate a two-dimensional extension of GLPP (2D-GLPP) as an example to show how to extend GLPP with some other statistical techniques. We apply our works to face recognition on four popular face databases, namely …
Depth reconstruction and hyperspectral reflectance reconstruction are two active research topics in computer vision and image processing. Conventionally, these two topics have been studied separately using independent imaging setups and there is no existing method which can acquire depth and spectral reflectance simultaneously in one shot without using special hardware. In this paper, we propose a novel single-shot hyperspectral-depth reconstruction method using an off-the-shelf RGB camera and projector. Our method is based on a single color-dot projection, which simultaneously acts as structu…
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）
University of Electronic Science and Technology of China（电子科技大学）；Hong Kong Baptist University（香港浸会大学）
In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accur…
Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；Shaoxing University（绍兴大学）；King Saud University（国王 Saud 大学）；Innopolis University（Innopolis 大学）；University of Messina（墨西拿大学）
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）
Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
Beijing Institute of Technology, Beijing 100081, China（北京理工大学）；Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China（北京理工大学重庆创新中心）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China（光电成像技术与系统重点实验室）
A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification
College of Engineering（工程学院）；School of Electronic Information and Communications（电子信息与通信学院）；Changchun Institute of Optics, Fine Mechanics and Physics（长春光学精密机械与物理研究所）；School of Computer Science and Technology（计算机科学与技术学院）；School of Intelligent Manufacturing and Electrical Engineering（智能制造与电气工程学院）
Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校先进技术开发中心）；Department of Electrical Engineering, Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校电气工程系）
Dimensionality reduction (DR) methods have attracted extensive attention to provide discriminative information and reduce the computational burden of the hyperspectral image (HSI) classification. However, the DR methods face many challenges due to limited training samples with high dimensional spectra. To address this issue, a graph-based spatial and spectral regularized local scaling cut (SSRLSC) for DR of HSI data is proposed. The underlying idea of the proposed method is to utilize the information from both the spectral and spatial domains to achieve better classification accuracy than its …
Northwestern Polytechnical University（西北工业大学）；Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（中国科学院西安光学精密机械研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ign…
SpectralTrain: A Universal Framework for Hyperspectral Image Classification
School of Medical Information, Wannan Medical University（皖南医学院信息学院）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong（香港中文大学）；Northeastern University（东北大学）
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as L1-Scaling Cut (L1-SC). The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data. Though L2-norm is generally preferred for computation, it is sensitive to noise and outliers. However, L1-norm is robust to them. Therefore, we obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm. Furthermore, an iterative algorithm is describ…
Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model
School of Computer Science and IT, Adelaide University（阿德莱德大学计算机科学与信息技术学院）；Department of Informatics, Institut Teknologi Sepuluh Nopember（泗水理工学院信息学系）；University of Technology, Sydney, Australia（悉尼科技大学）；Faculty of Computer Science, University of Indonesia（印度尼西亚大学计算机科学学院）
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, with more spectral bands for HSI, while the running time of these methods significantly increases, their denoising performance benefits little. In this paper, we claim that the HSI underlines a global spectral low-rank subspace, and the spectral subspaces of each full band patch groups should underlie this global low-rank subspace. This motivates us to propose a unified spatial-spectral paradigm for HSI denoising. As the new model is hard to optimize, …
Scalable Context-Preserving Model-Aware Deep Clustering for Hyperspectral Images
Ghent University（根特大学）；Royal Institute for Cultural Heritage (KIK-IRPA)（皇家文化遗产研究院）；China University of Geosciences（中国地质大学）；Vrije Universiteit Brussel（布鲁塞尔自由大学）
Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Peking University（北京大学）；National Center for Applied Mathematics Shenzhen (NCAMS), Southern University of Science and Technology（南方科技大学深圳应用数学国家中心）；Department of Automation, Tsinghua University（清华大学自动化系）；National Research Council of Italy, Institute of Integrated Methodologies for Earth Observation (CNR-IMIOT)（意大利国家研究委员会综合地球观测方法研究所）
We consider the problem of adapting a network trained on three-channel color images to a hyperspectral domain with a large number of channels. To this end, we propose domain adaptor networks that map the input to be compatible with a network trained on large-scale color image datasets such as ImageNet. Adaptors enable learning on small hyperspectral datasets where training a network from scratch may not be effective. We investigate architectures and strategies for training adaptors and evaluate them on a benchmark consisting of multiple hyperspectral datasets. We find that simple schemes such …
GIPSA-Lab（格勒诺布尔图像、信号、自动化与控制实验室）；Grenoble Institute of Technology（格勒诺布尔理工学院）；Faculty of Electrical and Computer Engineering, University of Iceland（冰岛大学电气与计算机工程学院）
Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions. These complementary characteristics have stimulated active research in the inference of images with high spatial and spectral resolutions from HSI-MSI pairs. In this paper, we formulate this data fusion problem as the minimization of a convex objective function containing two data-fitting terms and an edge-preserving regularizer. The data-fitting terms are quadratic and…
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum. Specifically, 3D convolution is utilized to extract structural spatio-spectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the global correlation along spectrum. Moreover, alternating directional structure is introduced to eliminate the causal dependency with no additional computation cost. The pro…
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…
Northwest A\&F University, College of Science, Yangling 712100, P.R. China
Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). Conventionally, the rank of LR matrix is approximated using nuclear norm (NN). The NN is defined by adding all singular values together, which is essentially a $L_1$-norm of the singular values. It results in non-negligible approximation errors and thus the resulting matrix estimator can be significantly biased. Moreover, these bandwise TV-based methods exploit the spatial information in a separate manner. To cope with these problems, we propose …
This work proposes an adaptive trace lasso regularized L1-norm based graph cut method for dimensionality reduction of Hyperspectral images, called as `Trace Lasso-L1 Graph Cut' (TL-L1GC). The underlying idea of this method is to generate the optimal projection matrix by considering both the sparsity as well as the correlation of the data samples. The conventional L2-norm used in the objective function is sensitive to noise and outliers. Therefore, in this work L1-norm is utilized as a robust alternative to L2-norm. Besides, for further improvement of the results, we use a penalty function of t…
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…
Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution
School of Computer Science and Technology, Xi’an Jiaotong University（计算机科学与技术学院，西安交通大学）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）
TUBITAK Space Technologies Research Institute（土耳其科学技术研究理事会空间技术研究所）；Middle East Technical University（中东技术大学）
Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so called \textit{EndNet}, that is based …
Nanjing University of Information Science and Technology（南京信息工程大学）；National University of Singapore（新加坡国立大学）
Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period. Its core issue is how to reconstruct the underlying hyperspectral image using compressive sensing reconstruction algorithms. Due to the diversity in the spectral response characteristics and wavelength range of different spectral imaging devices, previous works are often inadequate to capture complex spectral …
Northwestern Polytechnical University（西北工业大学）；University of New South Wales（新南威尔士大学）
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five bands with high similarity in a certain spectral range are divided into three…
China University of Geosciences（中国地质大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿罗森多夫亥姆霍兹中心）；Helmholtz Institute Freiberg for Resource Technology（弗赖贝格资源技术亥姆霍兹研究所）；Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；National University of Defense Technology（国防科技大学）
Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. S…
Revisiting Adversarial Training under Hyperspectral Image
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Engineering Research Center of Blockchain Application, Supervision and Management, Ministry of Education, Southeast University（东南大学教育部区块链应用监管工程研究中心）；Purple Mountain Laboratories（紫金山实验室）；Collaborative Innovation Center of Geospatial Technology, Wuhan University（武汉大学地理空间技术协同创新中心）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
A UNet Model for Accelerated Preprocessing of CRISM Hyperspectral Data for Mineral Identification on Mars
IPDF, Department of Civil Engineering, Indian Institute of Technology Guwahati, India（印度理工学院古瓦哈提分校土木工程系）；IPDF, Center for Intelligent Cyber Physical System, Indian Institute of Technology Guwahati, India（印度理工学院古瓦哈提分校智能网络物理系统中心）；Professor, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, India（印度卡纳塔克国家理工学院水资源与海洋工程系）；Associate Professor, Department of Civil Engineering, Indian Institute of Technology Guwahati, India（印度理工学院古瓦哈提分校土木工程系）
Iterative Low-rank Network for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Information and Communication Technology, Griffith University（信息与通信技术学院，格里菲斯大学）；College of Computer Science, Zhejiang University（计算机科学学院，浙江大学）
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…
Spectrum-driven Mixed-frequency Network for Hyperspectral Salient Object Detection
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（教育部光电成像技术与系统重点实验室）；Chongqing Innovation Center（重庆创新中心）
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）
The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called Graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs a…
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one. Noise contamination can often be caused during data acquisition and conversion. In this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the clean HSI data have its underlying local LR tensor property, even though the real HSI data may not be globally low-rank due to out-liers and non-Gaussian noise. According to this fact, we propose a novel tensor $L_γ$-norm to form…
Beijing University of Technology（北京工业大学）；The University of Sydney（悉尼大学）；Dalian University of Technology（大连理工大学）
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used d…
The goal of data selection is to capture the most structural information from a set of data. This paper presents a fast and accurate data selection method, in which the selected samples are optimized to span the subspace of all data. We propose a new selection algorithm, referred to as iterative projection and matching (IPM), with linear complexity w.r.t. the number of data, and without any parameter to be tuned. In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next i…
