This paper introduces the Class-wise Principal Component Analysis, a supervised feature extraction method for hyperspectral data. Hyperspectral Imaging (HSI) has appeared in various fields in recent years, including Remote Sensing. Realizing that information extraction tasks for hyperspectral images are burdened by data-specific issues, we identify and address two major problems. Those are the Curse of Dimensionality which occurs due to the high-volume of the data cube and the class imbalance problem which is common in hyperspectral datasets. Dimensionality reduction is an essential preprocessing step to complement a hyperspectral image classification task. Therefore, we propose a feature extraction algorithm for dimensionality reduction, based on Principal Component Analysis (PCA). Evaluations are carried out on the Indian Pines dataset to demonstrate that significant improvements are achieved when using the reduced data in a classification task.
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
School of Electronics and Information, Zhongyuan University of Technology（中原工学院电子与信息学院）；Birkbeck College, University of London（伦敦大学伯克贝克学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Information Center of the Yellow River Conservancy Commission（黄河水利委员会信息中心）
Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
CNES（法国国家空间研究中心）；Toulouse Mathematics Institute（图卢兹数学研究所）；CNRS（法国国家科学研究中心）；University of Toulouse（图卢兹大学）；ONERA-DOTA（法国航空航天研究院DOTA部门）；Magellium（马哲利姆公司）
针对现有高光谱数据集不适合评估泛化性能的问题，发布大规模Toulouse Hyperspectral Data Set基准，并基于Masked Autoencoder等自监督技术建立像素级分类基线，实现85%准确率。
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons
Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC)（拉斯帕尔马斯大学应用微电子研究所）；Universidad Politecnica de Madrid（马德里理工大学）
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shenzhen Institute of Future Media Technology（深圳未来媒体技术研究院）；Westlake University（西湖大学）；ETH Zürich（苏黎世联邦理工学院）
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The HSI representations are highly similar and correlated across the spectral dimension. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. However, existing CNN-based methods show limitations in capturing spectral-wise similarity and long-range dependencies. Besides, the HSI information is modulated by a coded aperture (physical mask) in CASSI. Nonetheless, current algorithms have not fully expl…
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
University Federico II（费德里科二世大学）；National Research Council, Institute of Methodologies for Environmental Analysis, CNR-IMAA（国家研究委员会环境分析方法研究所）；National Biodiversity Future Center（国家生物多样性未来中心）；University Parthenope（帕尔特诺珀大学）
Hyperspectral image (HSI) classification is the most vibrant area of research in the hyperspectral community due to the rich spectral information contained in HSI can greatly aid in identifying objects of interest. However, inherent non-linearity between materials and the corresponding spectral profiles brings two major challenges in HSI classification: interclass similarity and intraclass variability. Many advanced deep learning methods have attempted to address these issues from the perspective of a region/patch-based approach, instead of a pixel-based alternate. However, the patch-based app…
Silesian University of Technology（西里西亚工业大学）；KP Labs（KP实验室）
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks. It plays a pivotal role in remote-sensing scenarios in which the amount of high-quality ground truth data is limited, and acquiring new examples is costly or impossible. This is a common problem in hyperspectral imaging, where manual annotation of image data is difficult, expensive, and prone to human bias. In this letter, we propose online data augmentation of hyperspectral data which is executed during the inference rather than before the training of deep networks. This is in con…
Hamedan University of Technology（哈马丹工业大学）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）
This paper proposes a spatial feature extraction method based on energy of the features for classification of the hyperspectral data. A proposed orthogonal filter set extracts spatial features with maximum energy from the principal components and then, a profile is constructed based on these features. The important characteristic of the proposed approach is that the filter sets coefficients are extracted from statistical properties of data, thus they are more consistent with the type and texture of the remotely sensed images compared with those of other filters such as Gabor. To assess the per…
THAT: Token-wise High-frequency Augmentation Transformer for Hyperspectral Pansharpening
JPMorgan Chase（摩根大通公司）；University of Missouri-Kansas City（密苏里大学-堪萨斯城分校）；Ming Hsieh Department of Electrical and Computer Engineering（明希赫电气与计算机工程系）；University of Southern California（南加州大学）；Robinson Research Institute（罗宾逊研究学院）；KTH Royal Institute of Technology（皇家理工学院）；NEC Laboratories America（NEC美国实验室）；University of Virginia（弗吉尼亚大学）
RWTH Aachen University（亚琛工业大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial in hyperspectral image analysis. The spectral variability and noise in hyperspectral data make this task very challenging and define special requirements for such methods. Here, we present a novel unsupervised hyperspectral segmentation framework. It starts with a denoising and dimensionality reduction step by the well-est…
College of Computer Science, Sichuan University（四川大学计算机学院）
Hyperspectral Image(HSI) classification is the most vibrant field of research in the hyperspectral community, which aims to assign each pixel in the image to one certain category based on its spectral-spatial characteristics. Recently, some spectral-spatial-feature based DCNNs have been proposed and demonstrated remarkable classification performance. When facing a real HSI, however, these Networks have to deal with the pixels in the image one by one. The pixel-wise processing strategy is inefficient since there are numerous repeated calculations between adjacent pixels. In this paper, firstly,…
The fast development of self-supervised learning lowers the bar learning feature representation from massive unlabeled data and has triggered a series of research on change detection of remote sensing images. Challenges in adapting self-supervised learning from natural images classification to remote sensing images change detection arise from difference between the two tasks. The learned patch-level feature representations are not satisfying for the pixel-level precise change detection. In this paper, we proposed a novel pixel-level self-supervised hyperspectral spatial-spectral understanding …
Xi’an Jiaotong University（西安交通大学）；Xidian University（西安电子科技大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Columbia University（哥伦比亚大学）
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using s…
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Guangzhou University（广州大学）
National University of Defense Technology（国防科技大学）；National Innovation Institute of Defense Technology, Chinese Academy of Military Science（军事科学院国防科技创新研究院）
Nowadays, deep learning methods, especially the convolutional neural networks (CNNs), have shown impressive performance on extracting abstract and high-level features from the hyperspectral image. However, general training process of CNNs mainly considers the pixel-wise information or the samples' correlation to formulate the penalization while ignores the statistical properties especially the spectral variability of each class in the hyperspectral image. These samples-based penalizations would lead to the uncertainty of the training process due to the imbalanced and limited number of training…
This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize Principal Component Analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate regression to ensure the reduction in redundancy between he PCA coefficients, the reduction of the variance of the scores, and the reduction in the reconstruction error. More importantly, unlike other nonlinear dimensionality reduction methods, the invertibility, volume-preservati…
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）；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 spectra…
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Extremadura（埃斯特雷马杜拉大学）；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研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective…
A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver
Ruđer Bošković Institute（鲁杰尔·博什科维奇研究所）；Technical University Munich（慕尼黑工业大学）；University of Montreal（蒙特利尔大学）；Photon etc.（Photon etc.公司）；Clinical Hospital Center Zagreb（萨格勒布临床医院中心）；University of Zagreb（萨格勒布大学）；Clinical Hospital Dubrava（杜布拉瓦临床医院）
Qingdao University（青岛大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Kentucky（肯塔基大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the $\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which…
Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（机器智能与系统控制重点实验室，教育部）；University of California, Los Angeles（加州大学洛杉矶分校）；Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences（智能地理信息处理重点实验室，中国地质大学）；Lingnan University（岭大大学）
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Grenoble INP（格勒诺布尔国立理工学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Xi’an Jiaotong University（西安交通大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of Extremadura（埃斯特雷马杜拉大学）
To read the final version please go to IEEE TGRS on IEEE Xplore. Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations. Nevertheless, their ability in modeling relations between samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or non-grid) data representation and analysis. In this paper, we thoroughly investigate CNNs and GCNs (qualit…
Qingdao University（青岛大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Kentucky（肯塔基大学）
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising, which adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the low-rank or column-wise sparse properties for the component matrices, respectively.For the $\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator, which can be generally used in other problems. Extensive experiments on both simul…
A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer
College of Computer Science and Engineering, Shandong University of Science and Technology（计算机科学与工程学院，山东科技大学）；Gosci Information Technology Co.,Ltd.（Gosci信息技术有限公司）
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
China University of Geosciences（中国地质大学）；National University of Defense Technology（国防科技大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Computer Science Center (National Supercomputing Center in Jinan)（山东省计算中心（国家超级计算济南中心））；Hexagon AB（海克斯康集团）
3D Fourier-based Global Feature Extraction 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）
National Laboratory of Radar Signal Processing, Xidian University（西安电子科技大学雷达信号处理国家重点实验室）；Science and Technology on Multispectral Information Processing Laboratory, School of Automation, Huazhong University of Science and Technology（华中科技大学自动化学院多谱信息处理技术国家级重点实验室）
Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such noises (random noise, HSI denoising), blurs (Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral and spatial downsample, HSI super-resolution). Previous HSI restoration methods are designed for one specific task only. Besides, most of them start from the 1-D vector or 2-D matrix models and cannot fully exploit th…
Liaoning Technical University（辽宁工程技术大学）；Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences（中国科学院福建物质结构研究所）；East China Normal University（华东师范大学）；The Hong Kong Polytechnic University（香港理工大学）
Recently, convolutional neural networks (CNNs) have achieved excellent performances in many computer vision tasks. Specifically, for hyperspectral images (HSIs) classification, CNNs often require very complex structure due to the high dimension of HSIs. The complex structure of CNNs results in prohibitive training efforts. Moreover, the common situation in HSIs classification task is the lack of labeled samples, which results in accuracy deterioration of CNNs. In this work, we develop an easy-to-implement capsule network to alleviate the aforementioned problems, i.e., 1D-convolution capsule ne…
City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程研究中心）；Wake Forest University（维克森林大学）
In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify hyperspectral images, which makes full use of spatial and spectral information. The Shape-adaptive Reconstruction (SaR) is introduced to preprocess each pixel based on the Pearson Correlation between pixels in its shape-adaptive (SA) region. Support Vector Machines (SVMs) are trained to estimate the pixel-wise probability maps of each class. Then the Smoothed Total Variation (STV) model is applied to denoise and generate the final classification map…
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Mississippi State University（密西西比州立大学）
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention. It is necessary to train a model only on source domain (SD) and directly transferring the model to target domain (TD), when TD needs to be processed in real time and cannot be reused for training. Based on the idea of domain generalization, a Single-source Domain Expansion Network (SDEnet) is developed to ensure the reliability and effectiveness of domain extension. The method uses generative adversarial learning to train in SD and test in TD. A generator including semantic encoder and morph encoder…
University of the Witwatersrand（金山大学）；School of Computer Science and Applied Mathematics（计算机科学与应用数学系）
Dictionary learning and sparse coding have been widely studied as mechanisms for unsupervised feature learning. Unsupervised learning could bring enormous benefit to the processing of hyperspectral images and to other remote sensing data analysis because labelled data are often scarce in this field. We propose a method for clustering the pixels of hyperspectral images using sparse coefficients computed from a representative dictionary as features. We show empirically that the proposed method works more effectively than clustering on the original pixels. We also demonstrate that our approach, i…
Deep learning based methods have achieved remarkable success in image restoration and enhancement, but most such methods rely on RGB input images. These methods fail to take into account the rich spectral distribution of natural images. We propose a deep architecture, SpecNet, which computes spectral profile to estimate pixel-wise dynamic range adjustment of a given image. First, we employ an unpaired cycle-consistent framework to generate hyperspectral images (HSI) from low-light input images. HSI is further used to generate a normal light image of the same scene. We incorporate a self-superv…
Autonomous navigation in unstructured off-road environments is greatly improved by semantic scene understanding. Conventional image processing algorithms are difficult to implement and lack robustness due to a lack of structure and high variability across off-road environments. The use of neural networks and machine learning can overcome the previous challenges but they require large labeled data sets for training. In our work we propose the use of hyperspectral images for real-time pixel-wise semantic classification and segmentation, without the need of any prior training data. The resulting …
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…
Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach
FOSS Analytical A/S（福斯分析公司）；Department of Computer Science (DIKU), University of Copenhagen（哥本哈根大学计算机科学系）；Department of Food Science (UCPH FOOD), University of Copenhagen（哥本哈根大学食品科学系）；Information Technology Group, Wageningen University and Research (WUR)（瓦赫宁根大学及研究中心信息技术组）；Food and Biobased Research, Wageningen University and Research (WUR)（瓦赫宁根大学及研究中心食品与生物基研究）；Natural History Museum of Denmark (NHMD), University of Copenhagen（哥本哈根大学丹麦自然历史博物馆）
Local spectral similarity (LSS) algorithm has been developed for detecting homogeneous areas and edges in hyperspectral images (HSIs). The proposed algorithm transforms the 3-D data cube (within a spatial window) into a spectral similarity matrix by calculating the vector-similarity between the center pixel-spectrum and the neighborhood spectra. The final edge intensity is derived upon order statistics of the similarity matrix or spatial convolution of the similarity matrix with the spatial kernels. The LSS algorithm facilitates simultaneous use of spectral-spatial information for the edge det…
The mining and utilization of features directly affect the classification performance of models used in the classification and recognition of hyperspectral remote sensing images. Traditional models usually conduct feature mining from a single perspective, with the features mined being limited and the internal relationships between them being ignored. Consequently, useful features are lost and classification results are unsatisfactory. To fully mine and utilize image features, a new multi-scale feature-mining learning algorithm (MGRNet) is proposed. The model uses principal component analysis t…
CentraleSupélec（法国中央理工-高等电力学院）；Université Paris-Saclay（巴黎萨克雷大学）；American University of Beirut（贝鲁特美国大学）；ONERA（法国航空航天研究院）；National University of Singapore（新加坡国立大学）
In this work, a novel target detector for hyperspectral imagery is developed. The detector is independent on the unknown covariance matrix, behaves well in large dimensions, distributional free, invariant to atmospheric effects, and does not require a background dictionary to be constructed. Based on a modification of the robust principal component analysis (RPCA), a given hyperspectral image (HSI) is regarded as being made up of the sum of a low-rank background HSI and a sparse target HSI that contains the targets based on a pre-learned target dictionary specified by the user. The sparse comp…
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）；The University of Melbourne（墨尔本大学）
In recent years, deep convolutional neural networks (CNNs) have shown impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classification. However, the existing CNN-based models operate at the patch-level, in which pixel is separately classified into classes using a patch of images around it. This patch-level classification will lead to a large number of repeated calculations, and it is difficult to determine the appropriate patch size that is beneficial to classification accuracy. In addition, the conventional CNN models operate convolutions with…
