Article
Environmental Sciences
Ali Mohamed, Ashraf Emam, Basem Zoheir
Summary: Space-borne hyperspectral imagery data are useful for mineral mapping due to their high spectral resolution, but the available free hyperspectral scenes are limited in geographical coverage. Multispectral imagery scenes have wider wavelength intervals and better spatial coverage, but their low spectral resolution limits their efficiency in mineral mapping. This study presents a new transformation tool that can simulate hyperspectral sensor responses in unscanned areas by using partially overlapping hyperspectral and multispectral scenes. The results confirm the reliability of this transformation and show potential for automated and cost-free mineral exploration in vast areas.
Article
Computer Science, Information Systems
Jasurbek Gulomov, Oussama Accouche, Bilel Neji, Jakhongir Ziyoitdinov
Summary: The article highlights the role of simulation in solar cell research and introduces the newly-developed solar cell simulation program, Suntulip, written in C#. Suntulip demonstrates reliability and accuracy in optical simulations and offers a user-friendly interface with versatile capabilities.
Article
Geochemistry & Geophysics
Na Liu, Lu Li, Wei Li, Ran Tao, James E. Fowler, Jocelyn Chanussot
Summary: The study introduces a tensor-based fusion method that combines the benefits of multispectral and hyperspectral images to impose low-rank property directly in both spatial and spectral domains, demonstrating robustness in handling missing hyperspectral values.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Agriculture, Multidisciplinary
Anting Guo, Huichun Ye, Wenjiang Huang, Binxiang Qian, Jingjing Wang, Yubin Lan, Shizhou Wang
Summary: The accurate estimation of leaf area index (LAI) is crucial for evaluating crop growth in precision agriculture. This study constructed hybrid inversion models (HIMs) using UAV hyperspectral and multispectral data to estimate maize LAI. The results showed that the Gaussian process regression-based HIM with active learning (GPR-AL-HIM) achieved the best performance in LAI estimation. Furthermore, the study explored the effects of UAV spectral and image spatial resolution on LAI inversion, and accurately mapped the LAI distribution in the study area using the GPR-AL-HIM.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Agronomy
Nik Norasma Che'Ya, Ernest Dunwoody, Madan Gupta
Summary: The study successfully discriminated weed species in sorghum fields using hyperspectral data, which were later detected and analyzed using multispectral images. The results showed that the differences between weed species and sorghum could be successfully detected through this method, with the highest spatial resolution yielding the highest accuracy for weed detection.
Article
Optics
Suining Gao, Xiubin Yang, Li Jiang, Ziming Tu, Mo Wu, Zongqiang Fu
Summary: In this paper, a Content-aware Dynamic Filter salient object detection Network using visible and polarized mask images is proposed, which achieves superior detection results compared to state-of-the-art algorithms by utilizing prior information on polarization dimension to guide SOD.
Article
Environmental Sciences
Tobias Hupel, Peter Stuetz
Summary: This paper investigates and evaluates the applicability of four well-known hyperspectral anomaly detection methods and a new method called local point density for real-time camouflage detection in multispectral imagery. The results show that these methods have generally high detection performance on various targets.
Article
Engineering, Electrical & Electronic
Xiaoxiao Feng, Zhenfeng Shao, Xiao Huang, Luxiao He, Xianwei Lv, Qingwei Zhuang
Summary: This study proposes a deep feature fusion-based classification method to improve the mapping accuracy of impervious surface area (ISA) by integrating Zhuhai-1 hyperspectral (HS) imagery with Sentinel-2 multispectral (MS) imagery. The results demonstrate that the proposed method achieves high classification accuracy and robustness in highly urbanized study areas.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Sihan Huang, David W. Messinger
Summary: Improving the spatial resolution of hyperspectral images has always been an important topic in remote sensing. Most existing methods suffer from performance degradation and poor radiometric accuracy. To overcome these issues, this study proposes a stable hyperspectral sharpening method based on the Laplacian pyramid and the generative convolutional neural network. It achieves superior radiometric accuracy of the sharpened data in different up-scale ratios based on one single input pair.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Yongming Liu, Danling Tang, Ruru Deng, Bin Cao, Qidong Chen, Ruihao Zhang, Yan Qin, Shaoquan Zhang
Summary: The log-ratio method has been widely used for mapping bathymetry in oligotrophic waters, with selection criteria of bands impacting depth detection and sensitivity tradeoffs. This study applied global sensitivity analysis to determine wavelength band variations with water depth, leading to the development of an adaptive blended algorithm approach (ABAA) for improved depth estimation accuracy in different water depths. The ABAA outperforms the LRM and optimization-based methods, especially in shallow waters, when in situ bathymetry data are available.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Guichen Zhang, Paul Scheunders, Daniele Cerra, Rupert Mueller
Summary: In this article, a physics-based spectral mixture model called the extended shadow multilinear mixing (ESMLM) model is proposed, which can adapt to different ground surface scenarios with varying illumination conditions and shadows. The model performs robustly and provides physically interpretable parameters that contain valuable information on the scene structures.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Silvia Tozza, Dizhong Zhu, William A. P. Smith, Ravi Ramamoorthi, Edwin R. Hancock
Summary: In this paper, we present a method for estimating shape from polarisation and shading information under unknown illumination conditions. We propose alternative photo-polarimetric constraints and demonstrate how to express them using a unified system of partial differential equations, which allows for linear least squares solutions. We also introduce new methods for estimating polarisation images, albedo, and refractive index, and evaluate their performance on both synthetic and real-world data, showing improvements over existing state-of-the-art methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Environmental Sciences
Yingxi Wang, Ming Chen, Xiaotao Xi, Hua Yang
Summary: This paper investigates the use of hyperspectral satellite images for bathymetry inversion and proposes an attention-based band optimization one-dimensional convolutional neural network model (ABO-CNN) to better utilize the spectral information for bathymetry inversion. The results demonstrate that the ABO-CNN model outperforms other models in retrieving bathymetry information.
Article
Environmental Sciences
Rey Ducay, David W. Messinger
Summary: This article introduces a method for enhancing the spatial resolution of a hyperspectral image using a co-registered high-resolution multispectral image. It discusses the advantages and disadvantages of existing algorithms and deep learning methods. Experimental results demonstrate the superiority of the proposed NNDiffuse fusion method in target detection applications.
JOURNAL OF APPLIED REMOTE SENSING
(2023)
Article
Remote Sensing
Changwei Wang, Qi Chen, Haisheng Fan, Chaolong Yao, Xin Sun, Ji Chan, Jizhong Deng
Summary: Hyperspectral remote sensing technology has great potential for identifying crops, with OHS and S2B being more reliable than L8 for cotton identification. The overall accuracy of the three sensors is comparable, with OHS having the highest accuracy followed by S2B and L8.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Lu Bai, Lixin Cui, Yuhang Jiao, Luca Rossi, Edwin R. Hancock
Summary: In this paper, a novel backtrackless aligned-spatial graph convolutional network (BASGCN) model is proposed for learning effective features for graph classification. This model addresses the issues of information loss and imprecise information representation in existing spatially-based graph convolutional network (GCN) models, and bridges the theoretical gap between traditional convolutional neural network (CNN) models and spatially-based GCN models. Experimental results demonstrate the effectiveness of the proposed model.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xing Ai, Chengyu Sun, Zhihong Zhang, Edwin R. Hancock
Summary: The study proposes a novel GNN framework called TL-GNN, which combines subgraph-level information with node-level information to enrich the features captured by GNNs. The study also provides a mathematical analysis of the LPI problem and proposes a subgraph counting method based on the dynamic programming algorithm.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chen Wang, Xiang Wang, Jiawei Zhang, Liang Zhang, Xiao Bai, Xin Ning, Jun Zhou, Edwin Hancock
Summary: This paper proposes a novel approach to estimate uncertainties in stereo matching end-to-end, using the NIG distribution to calculate uncertainties and additional loss functions to enhance sensitivity and smoothness. Experimental results show that this method improves stereo matching results, particularly performing well on out-of-distribution data.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Lei Zhou, Yang Liu, Pengcheng Zhang, Xiao Bai, Lin Gu, Jun Zhou, Yazhou Yao, Tatsuya Harada, Jin Zheng, Edwin Hancock
Summary: Zero-shot learning aims to recognize novel classes by transferring semantic knowledge. The proposed bidirectional embedding based generative model introduces an information bottleneck constraint to preserve attribute information. Experimental results show that the method outperforms state-of-the-art methods on benchmark datasets.
Article
Computer Science, Artificial Intelligence
Xingchen Guo, Xuexin Xu, Xunquan Chen, Jinhui Chen, Rong Jia, Zhihong Zhang, Tetsuya Takiguchi, Edwin R. Hancock
Summary: This paper presents an effective method for multi-talker localization using only a single microphone in a room, which can successfully and accurately process the localization task. Experiments demonstrate the effectiveness of the proposed method.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Zhihong Zhang, Dongdong Chen, Lu Bai, Jianjia Wang, Edwin R. Hancock
Summary: This article introduces the efficient representation of network structure using motifs and studies the distribution of subgraphs using statistical mechanics to understand the motif structure of a network. By mapping network motifs to clusters in a gas model, the partition function for a network is derived to calculate global thermodynamic quantities. Analytical expressions for the number of specific types of motifs and their associated entropy are presented. Numerical experiments on synthetic and real-world data sets evaluate the qualitative and quantitative characteristics of motif entropy derived from the partition function. The motif entropy for real-world networks, such as financial stock market networks, is found to be sensitive to the variance in network structure, indicating well-defined information-processing functions of network motifs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lu Bai, Lixin Cui, Zhihong Zhang, Lixiang Xu, Yue Wang, Edwin R. Hancock
Summary: This paper presents a novel framework for computing kernel-based similarity measures between dynamic time-varying financial networks, which is used to analyze financial time series. The commute time (CT) matrix is computed to identify a reliable set of correlated time series and their associated probability distributions. The dominant probability distributions are then used to construct a Shannon entropy time series, which is further used to develop an entropic dynamic time warping kernel for financial time series analysis. Experimental results demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dongdong Chen, Yuxing Dai, Lichi Zhang, Zhihong Zhang, Edwin R. Hancock
Summary: This paper presents a novel neural framework that converts the graph matching problem into a linear assignment problem in a high-dimensional space. By leveraging relative position information at the node level and high-order structural arrangement information at the subgraph level, the method improves the performance of graph matching tasks and establishes reliable node-to-node correspondences.
PATTERN RECOGNITION
(2023)
Article
Automation & Control Systems
Yuqing Ma, Xianglong Liu, Shihao Bai, Lei Wang, Aishan Liu, Dacheng Tao, Edwin R. Hancock
Summary: In this study, a generic inpainting framework is proposed to handle incomplete images with both contiguous and discontiguous large missing areas. By employing an adversarial modeling and regionwise operations, the framework is able to generate semantically reasonable and visually realistic images, outperforming existing methods on large contiguous and discontiguous missing areas, as demonstrated by qualitative and quantitative experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Lu Bai, Yuhang Jiao, Lixin Cui, Luca Rossi, Yue Wang, Philip S. Yu, Edwin R. Hancock
Summary: This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network (GCNN) models, the proposed QSGCNN model incorporates the process of identifying transitive aligned vertices between graphs and transforms arbitrary sized graphs into fixed-sized aligned vertex grid structures. The effectiveness of the proposed QSGCNN model is demonstrated through experiments on benchmark graph classification datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Lixin Cui, Ming Li, Lu Bai, Yue Wang, Jing Li, Yanchao Wang, Zhao Li, Yunwen Chen, Edwin R. Hancock
Summary: This paper proposes a novel framework for computing Quantum-based Entropic Representations (QBER) for un-attributed graphs using Continuous-time Quantum Walk (CTQW). By transforming each original graph into a family of k-level neighborhood graphs, the framework captures multi-level topological information of the original global graph. The structure of each neighborhood graph is characterized using the Average Mixing Matrix (AMM) of CTQW, enabling the computation of Quantum Shannon Entropy and entropic signature. Experimental results demonstrate the effectiveness of the proposed approach in classification accuracies, outperforming other entropic complexity measuring methods, graph kernel methods, and graph deep learning methods.
PATTERN RECOGNITION
(2024)
Proceedings Paper
Computer Science, Artificial Intelligence
Lu Bai, Yuhang Jiao, Lixin Cui, Luca Rossi, Yue Wang, Philip S. Yu, Edwin R. Hancock
Summary: This paper proposes a novel Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. The main idea is to define a new quantum-inspired spatial graph convolution associated with pre-transformed fixed-sized aligned grid structures of graphs, in terms of quantum information propagation between grid vertices of each graph. It effectively reduces the information loss or the notorious tottering problem arising in existing spatially-based Graph Convolutional Network (GCN) models.
2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022)
(2022)
Article
Computer Science, Artificial Intelligence
Silvia Tozza, Dizhong Zhu, William A. P. Smith, Ravi Ramamoorthi, Edwin R. Hancock
Summary: In this paper, we present a method for estimating shape from polarisation and shading information under unknown illumination conditions. We propose alternative photo-polarimetric constraints and demonstrate how to express them using a unified system of partial differential equations, which allows for linear least squares solutions. We also introduce new methods for estimating polarisation images, albedo, and refractive index, and evaluate their performance on both synthetic and real-world data, showing improvements over existing state-of-the-art methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Geochemistry & Geophysics
W. A. P. Smith, P. Lewinska, M. A. Cooper, E. R. Hancock, J. A. Dowdeswell, D. M. Rippin
Summary: This paper studies the problem of structure-from-motion for images with varying principal point. Initialization and pose estimation methods specific to this scenario are proposed and the performance is demonstrated on challenging real-world examples.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geography, Physical
Michael A. Cooper, Paulina Lewinska, William A. P. Smith, Edwin R. Hancock, Julian A. Dowdeswell, David M. Rippin
Summary: This study presents an approach to extract quantifiable information from archival aerial photographs to extend the record of change in central eastern Greenland Ice Sheet. The insights gained from a longer record of ice margin change are crucial for understanding glacier response to climate change. The study also focuses on relatively small and understudied outlet glaciers from the eastern margin of the ice sheet, revealing significant heterogeneity in their response with non-climatic controls playing a key role.
