Deep learning methods have shown considerable potential for hyperspectral image (HSI) classification, which can achieve high accuracy compared with traditional methods. However, they often need a large number of training samples and have a lot of parameters and high computational overhead. To solve these problems, this paper proposes a new network architecture, LiteDepthwiseNet, for HSI classification. Based on 3D depthwise convolution, LiteDepthwiseNet can decompose standard convolution into depthwise convolution and pointwise convolution, which can achieve high classification performance with minimal parameters. Moreover, we remove the ReLU layer and Batch Normalization layer in the original 3D depthwise convolution, which significantly improves the overfitting phenomenon of the model on small sized datasets. In addition, focal loss is used as the loss function to improve the model's attention on difficult samples and unbalanced data, and its training performance is significantly better than that of cross-entropy loss or balanced cross-entropy loss. Experiment results on three benchmark hyperspectral datasets show that LiteDepthwiseNet achieves state-of-the-art performance with a very small number of parameters and low computational cost.
Hyperspectral Image (HSI) classification based on deep learning has been an attractive area in recent years. However, as a kind of data-driven algorithm, deep learning method usually requires numerous computational resources and high-quality labelled dataset, while the cost of high-performance computing and data annotation is expensive. In this paper, to reduce dependence on massive calculation and labelled samples, we propose a lightweight network architecture (LiteDenseNet) based on DenseNet for Hyperspectral Image Classification. Inspired by GoogLeNet and PeleeNet, we design a 3D two-way de…
MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
College of Engineering and IT, University of Dubai（迪拜大学工程与信息技术学院）；Department of Computer Science and Engineering, Alipurduar Government Engineering and Management College（阿利普杜尔政府工程与管理学院计算机科学与工程系）；Department of Geography, Simon Fraser University（西蒙·弗雷泽大学地理系）
Hyperspectral imagery is rich in spatial and spectral information. Using 3D-CNN can simultaneously acquire features of spatial and spectral dimensions to facilitate classification of features, but hyperspectral image information spectral dimensional information redundancy. The use of continuous 3D-CNN will result in a high amount of parameters, and the computational power requirements of the device are high, and the training takes too long. This letter designed the Faster selective kernel mechanism network (FSKNet), FSKNet can balance this problem. It designs 3D-CNN and 2D-CNN conversion modul…
HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification
College of Mathematics and Informatics, South China Agricultural University（华南农业大学数学与信息学院）；Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs（热带南方智能农业技术重点实验室，农业部）；School of Chemistry and Chemical Engineering, South China University of Technology（华南理工大学化学与化工学院）
National University of Computer and Emerging Sciences（国立计算机与新兴科学大学）；University of Messina（墨西拿大学）；Bahauddin Zakariya University（巴哈丁·扎卡里亚大学）；Innopolis University（因诺波利斯大学）
Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral…
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）
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）
Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification. In FPGA, an encoder-decoder based FCN is utilized to consider the global spatial information by processing the whole image, which results in fast inference. However…
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…
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）
HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；School of Electronic Information, Central South University（电子信息学院，中南大学）；Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University（遥感与地理信息系统研究所，地球与空间科学学院，北京大学）；School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology（遥感与地理信息工程学院，南京信息工程大学）；College of Electrical and Information Engineering, Hunan University（电气与信息工程学院，湖南大学）；Peng Cheng Laboratory（鹏城实验室）
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
Department of Computer Science, Aalto University, Finland（艾尔沃斯大学计算机科学系）；School of Electrical and Electronic Engineering, University of Sheffield, United Kingdom（谢菲尔德大学电子与电气工程学院）
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；SZU Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院深圳大学分部）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）
With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI c…
Real-time remote sensing applications like search and rescue missions, military target detection, environmental monitoring, hazard prevention and other time-critical applications require onboard real time processing capabilities or autonomous decision making. Some unmanned remote systems like satellites are physically remote from their operators, and all control of the spacecraft and data returned by the spacecraft must be transmitted over a wireless radio link. This link may not be available for extended periods when the satellite is out of line of sight of its ground station. Therefore, ligh…
Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS Grenoble INP（法国国家科学研究中心格勒诺布尔国立理工学院）；GIPSA-Lab（格勒诺布尔图像、信号、声学与自动化实验室）；Institut Universitaire de France (IUF)（法国大学研究院）
BCG-Former: Toward Pareto-Efficient Hyperspectral Image Classification via Band-Contextual Gating
College of Information Science, University of Arizona（亚利桑那大学信息科学学院）；Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution
Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（海洋科学与空间信息学院，中国石油大学（华东））；Department of Automation, Tsinghua University（自动化系，清华大学）
The application of infrared hyperspectral imagery to geological problems is becoming more popular as data become more accessible and cost-effective. Clustering and classifying spectrally similar materials is often a first step in applications ranging from economic mineral exploration on Earth to planetary exploration on Mars. Semi-manual classification guided by expertly developed spectral parameters can be time consuming and biased, while supervised methods require abundant labeled data and can be difficult to generalize. Here we develop a fully unsupervised workflow for feature extraction an…
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…
Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control
Department of Electrical Engineering and Information Technology, University Federico II（费米二世大学电气工程与信息科技系）；National Research Council, Institute of Methodologies for Environmental Analysis（国家研究 council 环境分析方法研究所）；Department of Engineering, University Parthenope（帕尔泰诺大学工程系）
Recently, convolutional neural networks (CNN) have obtained promising results in single-image SR for hyperspectral pansharpening. However, enhancing CNNs' representation ability with fewer parameters and a shorter prediction time is a challenging and critical task. In this paper, we propose a novel multi-spectral image fusion method using a combination of the previously proposed 3D CNN model VolumeNet and 2.5D texture transfer method using other modality high resolution (HR) images. Since a multi-spectral (MS) image consists of several bands and each band is a 2D image slice, MS images can be …
The hyperspectral image (HSI) has been widely used in many applications due to its fruitful spectral information. However, the limitation of imaging sensors has reduced its spatial resolution that causes detail loss. One solution is to fuse the low spatial resolution hyperspectral image (LR-HSI) and the panchromatic image (PAN) with inverse features to get the high-resolution hyperspectral image (HR-HSI). Most of the existing fusion methods just focus on small fusion ratios like 4 or 6, which might be impractical for some large ratios' HSI and PAN image pairs. Moreover, the ill-posedness of re…
Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat
National Cheng Kung University（成功大学）；Institute of Data Science（数据科学研究所）；Miin Wu School of Computing（米润计算机学院）；Department of Statistics（统计学系）；Department of Computer Science（计算机科学系）
City University of Hong Kong（香港城市大学）；Hong Kong Baptist University（香港浸会大学）；Huaqiao University（华侨大学）；University of Macau（澳门大学）
This paper explores the problem of hyperspectral image (HSI) super-resolution that merges a low resolution HSI (LR-HSI) and a high resolution multispectral image (HR-MSI). The cross-modality distribution of the spatial and spectral information makes the problem challenging. Inspired by the classic wavelet decomposition-based image fusion, we propose a novel \textit{lightweight} deep neural network-based framework, namely progressive zero-centric residual network (PZRes-Net), to address this problem efficiently and effectively. Specifically, PZRes-Net learns a high resolution and \textit{zero-c…
