Article
Environmental Sciences
Xiaohui Ding, Yong Li, Ji Yang, Huapeng Li, Lingjia Liu, Yangxiaoyue Liu, Ce Zhang
Summary: The PAR-ACaps, an adaptive capsule network, was proposed to address the gradient vanishing issue in hyperspectral remote sensing classification, achieving higher classification accuracy compared to benchmarks like RF, SVM, 1DCNN, CNN, 3DCNN, Caps, and ACaps with comparable network architectures.
Article
Remote Sensing
Yanfei Zhong, Xinyu Wang, Shaoyu Wang, Liangpei Zhang
Summary: This paper discusses the recent progress in Chinese spaceborne HRS, including typical satellite systems, data processing, and applications, as well as the future development trends of HRS in China.
GEO-SPATIAL INFORMATION SCIENCE
(2021)
Article
Geochemistry & Geophysics
Yuting Wan, Chao Chen, Ailong Ma, Liangpei Zhang, Xunqiang Gong, Yanfei Zhong
Summary: This article introduces a novel adaptive multistrategy particle swarm optimization (AMSPSO) method for hyperspectral image remote sensing band selection. The method utilizes the quotient of linear discriminant value and mean mutual information to remove redundancy between bands. By dynamically adjusting the motion parameters, the method is able to balance global and local capabilities.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Review
Environmental Sciences
Bowen Chen, Liqin Liu, Zhengxia Zou, Zhenwei Shi
Summary: This paper reviews representative methods for hyperspectral image target detection, and categorizes them into seven categories: hypothesis testing-based methods, spectral angle-based methods, signal decomposition-based methods, constrained energy minimization (CEM)-based methods, kernel-based methods, sparse representation-based methods, and deep learning-based methods. The basic principles, classical algorithms, advantages, limitations, and connections of these methods are comprehensively summarized, and critical comparisons are made on the summarized datasets and evaluation metrics. Furthermore, the future challenges and directions in the area are analyzed.
Article
Physics, Multidisciplinary
Taro Tezuka, Shizuma Namekawa
Summary: Task-nuisance decomposition explains why the information bottleneck loss is a suitable objective for supervised learning. By demonstrating that conditional mutual information provides an alternative upper bound for I(z;n), even if z is not a sufficient representation of x, we extend this framework.
Article
Environmental Sciences
Md Palash Uddin, Md Al Mamun, Md Ali Hossain, Masud Ibn Afjal
Summary: Hyperspectral images contain important information of land objects acquired through numerous narrow and contiguous spectral bands. Different strategies of feature extraction and selection are used to enhance the classification results. Despite the common use of PCA, it may not effectively capture local and subtle structures in HSIs. New methods like SFPCA and SSFPCA outperform traditional approaches by applying FPCA on highly correlated and spectrally grouped HSI bands.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Li He, Shuang-Li Qi, Jian-Zhao Duan, Tian-Cai Guo, Wei Feng, De-Xian He
Summary: The study aimed to improve monitoring accuracy of wheat powdery mildew severity by developing a novel vegetation index and identifying suitable observation angles. Results showed that the new spectral parameter suitable for disease index inversion was RPMI, with the best optimal observation angle at 10 degrees. RPMI not only enhances monitoring accuracy at a single angle but also provides stable accuracy in the 0 to 30 range in the forward direction.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geosciences, Multidisciplinary
Cheng Liu, Chengzhi Xing, Qihou Hu, Shanshan Wang, Shaohua Zhao, Meng Gao
Summary: This article reviews the recent advances in hyperspectral remote sensing techniques and discusses the future application prospects in air pollution monitoring. It recommends the use of a multi-means joint hyperspectral stereoscopic remote sensing monitoring mode for effective monitoring and regulation of air pollution.
EARTH-SCIENCE REVIEWS
(2022)
Review
Environmental Sciences
Alireza Sanaeifar, Ce Yang, Miguel de la Guardia, Wenkai Zhang, Xiaoli Li, Yong He
Summary: Recent advances and challenges in using proximal hyperspectral sensing for assessing plant abiotic stresses have been critically reviewed. This technique provides high-resolution images for studying plant physiology and monitoring spatio-temporal variations. The comprehensive review of 362 research papers shows the wide range of applications for detecting different types of abiotic stresses in plants.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Geochemistry & Geophysics
Xiumei Chen, Xiangtao Zheng, Yue Zhang, Xiaoqiang Lu
Summary: This letter proposes a local-global mutual learning (LML) approach to capture both the global and local features of remote sensing scene classification (RSSC). The method generates local regions by highlighting semantic areas in the original image and uses a two-branch architecture to extract features for the local regions and global image. Experimental results demonstrate the effectiveness of the proposed method.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Information Systems
Suting Chen, Meng Jin, Jie Ding
Summary: The study introduces a novel technique based on a dense residual three-dimensional convolutional neural network to address the issues of accuracy and efficiency in hyperspectral image classification. Experimental results show that the proposed technique significantly outperforms existing deep learning techniques in accuracy and training time.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Forestry
Ping Wang, Sanqing Tan, Gui Zhang, Shuang Wang, Xin Wu
Summary: This study uses the Lasso algorithm and support vector regression (SVR) model to estimate the aboveground biomass of the Lutou Forest Farm. The results show that the model can explain 73% of the aboveground biomass with high precision.
Article
Environmental Sciences
Ian J. Marang, Patrick Filippi, Tim B. Weaver, Bradley J. Evans, Brett M. Whelan, Thomas F. A. Bishop, Mohammed O. F. Murad, Dhahi Al-Shammari, Guy Roth
Summary: The research utilizes hyperspectral imaging combined with machine learning to accurately assess cotton crop nitrogen status, showing great potential in predicting nitrogen concentration and evaluating different treatment blocks. The study results demonstrate the effectiveness of hyperspectral imaging in predicting crop nitrogen concentration, indicating its significant potential in precision agriculture applications.
Article
Environmental Sciences
Wenmei Li, Huaihuai Chen, Qing Liu, Haiyan Liu, Yu Wang, Guan Gui
Summary: This article introduces a solution to the classification of hyperspectral remote sensing images by introducing an attention mechanism and depthwise separable convolution to a three-dimensional convolutional neural network. The proposed models, 3DCNN-AM and 3DCNN-AM-DSC, have been shown to improve classification accuracy and reduce computing time.
Article
Agronomy
Yuanyuan Fu, Guijun Yang, Ruiliang Pu, Zhenhai Li, Heli Li, Xingang Xu, Xiaoyu Song, Xiaodong Yang, Chunjiang Zhao
Summary: Nitrogen is closely related to crop photosynthetic capacity, and over-and-under-application of N fertilizers can lead to negative impacts on both crop productivity and the environment. Hyperspectral remote sensing, particularly using vegetation indices and machine learning algorithms, has emerged as a cost-effective alternative for determining crop N status. Further exploration of deep learning algorithms in this field is needed for more accurate assessment.
EUROPEAN JOURNAL OF AGRONOMY
(2021)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)