HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；school of software（软件学院）
Yang Liu, Jiahua Xiao, Xiang Song, Yu Guo, Peilin Jiang, Haiwei Yang, Fei Wang
Effectively modeling global context information in hyperspectral image (HSI) denoising is crucial, but prevailing methods using convolution or transformers still face localized or computational efficiency limitations. Inspired by the emerging Selective State Space Model (Mamba) with nearly linear computational complexity and efficient long-term modeling, we present a novel HSI denoising network named HSIDMamba (HSDM). HSDM is tailored to exploit the capture of potential spatial-spectral dependencies effectively and efficiently for HSI denoising. In particular, HSDM comprises multiple Hyperspectral Continuous Scan Blocks (HCSB) to strengthen spatial-spectral interactions. HCSB links forward and backward scans and enhances information from eight directions through the State Space Model (SSM), strengthening the context representation learning of HSDM and improving denoising performance more effectively. In addition, to enhance the utilization of spectral information and mitigate the degradation problem caused by long-range scanning, spectral attention mechanism. Extensive evaluations against HSI denoising benchmarks validate the superior performance of HSDM, achieving state-of-the-art performance and surpassing the efficiency of the transformer method SERT by 31%.
Nanjing University of Information Science and Technology（南京信息工程大学）
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Beside…
BRF-GS: Hyperspectral Bidirectional Reflectance Factor Modeling and Image Generation Based on 3D Gaussian Splatting
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；International Research Center of Big Data for Sustainable Development Goals（国际可持续发展大数据研究中心）；University of Chinese Academy of Sciences（中国科学院大学）
Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Department of Agriculture and Fisheries, Queensland Government（昆士兰州政府农业与渔业部）；School of Information Science and Engineering, Yantai University（烟台大学信息科学与工程学院）
提出融合双向反向 CNN、空间分析模块与注意力式动态特征提取的 SS non-Linear Model，在三个 HSI 数据集上以较低计算成本超越现有方法。
In the feature classification domain, the choice of data affects widely the results. The Hyperspectral image (HSI), is a set of more than a hundred bidirectional measures (called bands), of the same region (called ground truth map: GT). The HSI is modelized at a set of N vectors. So we have N features (or attributes) expressing N vectors of measures for C substances (called classes). The problematic is that it's pratically impossible to investgate all possible subsets. So we must find K vectors among N, such as relevant and no redundant ones; in order to classify substances. Here we introduce …
HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Queensland Department of Agriculture and Fisheries（昆士兰州农业与渔业厅）
ShanghaiTech University（上海科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Plex-VR Inc.（Plex-VR 公司）；Microsoft Research（微软研究院）；University of Delaware（特拉华大学）
In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 x 6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spec…
Hyperspectral images (HSI) classification is a high technical remote sensing tool. The main goal is to classify the point of a region. The HIS contains more than a hundred bidirectional measures, called bands (or simply images), of the same region called Ground Truth Map (GT). Unfortunately, some bands contain redundant information, others are affected by the noise, and the high dimensionalities of features make the accuracy of classification lower. All these bands can be important for some applications, but for the classification a small subset of these is relevant. In this paper we use mutua…
Omni-Fusion of Spatial and Spectral for Hyperspectral Image Segmentation
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China（多维信息处理上海实验室，华东师范大学，上海200241，中国）
DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Guangzhou University（广州大学）；University of Macau（澳门大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Mohamed V - Agdal University（穆罕默德五世-阿格达尔大学）；Mohamed V - Souissi University（穆罕默德五世-苏伊西大学）
Remote sensing is a technology to acquire data for disatant substances, necessary to construct a model knowledge for applications as classification. Recently Hyperspectral Images (HSI) becomes a high technical tool that the main goal is to classify the point of a region. The HIS is more than a hundred bidirectional measures, called bands (or simply images), of the same region called Ground Truth Map (GT). But some bands are not relevant because they are affected by different atmospheric effects; others contain redundant information; and high dimensionality of HSI features make the accuracy of …
Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification
Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences（泉州装备制造研究所、海西院、中国科学院）；Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences（福建物质结构研究所、中国科学院）；Liaoning Technical University（辽宁技术大学）；State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences（区域与城市生态国家重点实验室、城市环境研究所、中国科学院）
Faculty of Sciences, Mohamed V - Agdal University（穆罕默德五世-阿格达勒大学理学院）；ENSET, Mohamed V - Souissi University（穆罕默德五世-苏维西大学高等师范教育与技术学院）
Hyperspectral images (HIS) classification is a high technical remote sensing tool. The goal is to reproduce a thematic map that will be compared with a reference ground truth map (GT), constructed by expecting the region. The HIS contains more than a hundred bidirectional measures, called bands (or simply images), of the same region. They are taken at juxtaposed frequencies. Unfortunately, some bands contain redundant information, others are affected by the noise, and the high dimensionality of features made the accuracy of classification lower. The problematic is how to find the good bands to…
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
该研究提出 SSUMamba，通过六种空间-光谱连续扫描顺序、双向 SSM 与 3D 卷积高效建模高光谱图像的长程及局部依赖，在降低每批内存消耗的同时提升去噪性能。
CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Institute of Engineering and Management (IEM), University of Engineering and Management Kolkata, IEM Centre of Excellence for InnovAI, Department of Computer Science & Engineering（工程管理学院（IEM）、工程管理大学科塔卡尔，创新AI研究中心、计算机科学与工程系）；Institute of Engineering and Management (IEM), University of Engineering and Management Kolkata, Department of Computer Science & Engineering（工程管理学院（IEM）、工程管理大学科塔卡尔，计算机科学与工程系）
