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-supervised training methods in the field of HSI classification are based on the reconstruction of input samples, and it is difficult to achieve effective use of unlabeled samples. To address the shortcomings of CNN networks, we propose a noval multi-scale convolutional embedding module for HSI to realize effective extraction of spatial-spectral information, which can be better combined with Transformer network.In order to make more efficient use of unlabeled data, we propose a new self-supervised pretask. Similar to Mask autoencoder, but our pre-training method only masks the corresponding token of the central pixel in the encoder, and inputs the remaining token into the decoder to reconstruct the spectral information of the central pixel.Such a pretask can better model the relationship between the central feature and the domain feature, and obtain more stable training results.
Booz Allen Hamilton Inc.（博思艾伦汉密尔顿公司）；US Army Research Laboratory（美国陆军研究实验室）
We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pre-training effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset. This approach effectively resolves the architectural issue that arises when transferring meaningful information between the source and the target networks. To answer t…
Center for Applied Mathematics, Tianjin University（天津大学应用数学中心）
In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intra-class features are gathered while inter-class variations are enlarged. Based on the learned…
We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. In many settings this has proven to be more successful than other semi-supervised techniques, such as pretraining using unlabeled data. We furthermore show that the convolutional Ladder Network outperforms most of the current techniques used in hyperspectral image classification and achieves new state-of-the-art performance on the Pavia Un…
To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary pattern (LBP) to extract spatial features, and feature superposition to obtain the fused features of hyperspectral images. A new swarm intelligence optimization method with high convergence and strong global search capability, the Sparrow Search Algorithm (SSA), is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KEL…
Northwestern Polytechnical University（西北工业大学）；Université Sorbonne Paris Nord（索邦巴黎北大学）；Xidian University（西安电子科技大学）
To benefit the complementary information between heterogeneous data, we introduce a new Multimodal Transformer (MMFormer) for Remote Sensing (RS) image classification using Hyperspectral Image (HSI) accompanied by another source of data such as Light Detection and Ranging (LiDAR). Compared with traditional Vision Transformer (ViT) lacking inductive biases of convolutions, we first introduce convolutional layers to our MMFormer to tokenize patches from multimodal data of HSI and LiDAR. Then we propose a Multi-scale Multi-head Self-Attention (MSMHSA) module to address the problem of compatibilit…
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）；Université Côte d’Azur（蔚蓝海岸大学）；Northeastern University（东北大学）；Catholic University of Pelotas（佩洛塔斯天主教大学）
Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which lead to large scale nonsmooth ill-posed inverse problems. Furthermore, the regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions that further increase the complexity of the resulting optimization problem. In this …
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）；CNRS（法国国家科学研究中心）；OCA（蔚蓝海岸天文台）
Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can benefit from spatial regularization strategies. While existing spatial regularization methods improve the problem conditioning and promote piecewise smooth solutions, they lead to large nonsmooth optimization problems. Thus, efficiently introducing spatial context in the unmixing problem remains a challenge, and a necessity for many real world applications. In this pa…
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）
A Cross-Hierarchical Difference Feature Fusion Network Based on Multiscale Encoder-Decoder for Hyperspectral Change Detection
School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；Department of Artificial Intelligence, School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院人工智能系）；Department of Management Information Systems, King Faisal University（国王法赫德大学管理信息系统系）
Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images. The M-SRDL clustering algorithm extracts clusterings at many scales from a hyperspectral image and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spat…
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification
Nanjing University of Information Science and Technology（南京信息工程大学）；Institute of Optics and Electronics（光学电子研究所）；China University of Geosciences（中国地质大学）；University of Extremadura（埃斯特雷马杜拉大学）
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vect…
A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm
Universidade Federal de Santa Catarina（圣卡塔琳娜联邦大学）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；CRAN（CRAN 研究中心）；Universidade Católica de Pelotas（佩洛塔斯天主教大学）
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
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；University of Science and Technology of China（中国科学技术大学）
Physics-Informed Untrained Learning for RGB-Guided Superresolution Single-Pixel Hyperspectral Imaging
College of Science, Northwest Agriculture & Forestry University（西北农林科技大学理学院）；Key Laboratory of Photoelectronic Imaging Technology and System, School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院光电成像技术与系统教育部重点实验室）；State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学数字制造装备与技术国家重点实验室）
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this paper, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual-channel spatial, spectral and multiscale attention convolutional long short-term memory neural network (called dual-channel A3CLNN) for feature extraction and classification of multisource remote sensing data. Spatial, spectral and multiscale attent…
Couger Inc（库格公司）；Thiagarajar college of Engineering（蒂亚加拉贾工程学院）
Hyperspectral Imaging is the acquisition of spectral and spatial information of a particular scene. Capturing such information from a specialized hyperspectral camera remains costly. Reconstructing such information from the RGB image achieves a better solution in both classification and object recognition tasks. This work proposes a novel light weight network with very less number of parameters about 233,059 parameters based on Residual dense model with attention mechanism to obtain this solution. This network uses Coordination Convolutional Block to get the spatial information. The weights fr…
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）
