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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Roy, Sujeet Govil, Himanshu Mukherjee, Sandip Diwan, Prabhat Department of Applied Geology National Institute of Technology Raipur India Indian Institute of Remote Sensing Dehradun India

ISBN: (纸本)9798331530402

Ground Penetration Radar (GPR) is a remote sensing technology with immense potential. Its performance and quality of results strongly depends on primarily the composition of the soil and further on other aspects of the subsurface targets like the size, material, shape, depth, and orientation. The central operating frequency of the GPR and the type of antenna on the GPR are selected based on the factors above, to be able to detect your intended target satisfactorily. Limited exploratory research work has been undertaken in this field in India towards detection of small shallow buried objects. The present study attempts to experimentally investigate the combined effect of predetermined size and orientation of small shallow buried objects in conjunction with a suitably selected central frequency of the GPR. The experiment was conducted in Jaisalmer, where the arid dry sandy wastelands are conducive to GPR performance. Signal processing methods have been applied to the standard GPR results to derive better output. Apart from the standard effect of material of the targets and the depth buried to, the study mainly provided data indicating a positive correlation between the central operating frequency of the GPR and the quality of detection of the shallow subsurface targets of size corresponding to the wavelength λ The positive correlation of polarization of the GPR antenna to the alignment of the buried object was also noted. Further investigations covering more factors affecting GPR performance is advocated by the present study. © 2025 IEEE.

关键词： Binary alloys

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Using ontology and rules to retrieve the semantics of disaster remote sensing data

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Journal of Systems Engineering and Electronics 2024年第5期35卷 1211-1218页

作者： DONG Yumin LI Ziyang LI Xuesong LI Xiaohui Key Laboratory of Quantitative Remote Sensing Information Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

remote sensing data plays an important role in natural disaster ***,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing *** improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and *** prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

关键词： remote sensing data disaster ontology semantic reasoning

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Point-of-interest recommendation based on LBSN with multi-aspect fusion of social and individual features

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Neural Computing and Applications 2024年第20期36卷 12163-12184页

作者： Zhang, Yishan Liu, Yu College of Mining Engineering Taiyuan University of Technology Taiyuan030024 China Institute of Remote Sensing and Geographic Information Peking University Beijing100871 China

Point-of-interest (POI) recommendation is of paramount importance to user travel efficiency since users always expect their intended POIs can be recommended within a specified geospatial range. However, existing methods may not well establish user–user, user–POI, and POI–POI relatedness in a feature-interaction manner and social influence may not be infused into the recommendation process through feature fusion, causing unstable recommendation accuracy in different real-world datasets with multiple variables. A multi-aspect fusion of social and individual features POI recommendation method is proposed in this study, establishing feature interaction gate with user check-in ability, POI popularity, and POI physical characteristics involved. Furthermore, these multi-aspect feature interactions are exploited to incorporate multimodal data and establish information sharing and delivery between users in internal fusion through embedded factorization machine variants imposing individual influence and social influence in location-based social network (LBSN) on recommendation results. Moreover, relatedness enhancement module is established to balance contextual influence and social influence on user next movement decision in external fusion such that direct external information can be transmitted and shared, which diversifies recommendation results. Extensive experiments are conducted on two real-world datasets, and the results show that the proposed model achieves significantly superiority compared with its state-of-the-art baseline models and effectiveness of each proposed modules. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Economic and social effects

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Lung Cancer Detection with Machine Learning and Deep Learning: A Narrative Review 6

Lung Cancer Detection with Machine Learning and Deep Learnin...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Khouadja, Omar Naceur, Mohamed Saber National Institute of Applied Sciences and Technology Remote Sensing Laboratory and Information Systems Spatial Reference Tunis Tunisia

ISBN: (纸本)9798350321029

Lung cancer is the most common cancer-related cause of death worldwide. Unfortunately, current diagnostic techniques often lack sensitivity and precision, leading to delayed diagnoses and ineffective treatments. To diagnose lung cancer, doctors currently mainly rely on the clinical characteristics of their patients and imaging characteristics. However, these techniques have limitations in fully and promptly detecting lesions. Nevertheless, with the help of artificial intelligence (AI), lung cancer treatment, prognosis prediction, and diagnostics can be greatly improved. This paper provides an overview of the role that AI can play in simplifying tasks, while reducing the effort required of radiologists and increasing the accuracy of nodule detection. © 2023 IEEE.

关键词： Diseases

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Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region

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Big Earth Data 2023年第2期7卷 298-317页

作者： Min Jiang Li Jia Massimo Menenti Yelong Zeng State Key Laboratory of Remote Sensing Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Department of Geoscience and Remote Sensing Delft University of TechnologyDelftThe Netherlands University of Chinese Academy of Sciences BeijingChina

The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.

关键词： Vegetation greenness driving factors Sahel-Sudano-Guinean principal component regression

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An Overview of PSCCH Design and Performance Evaluation 4th

An Overview of PSCCH Design and Performance Evaluation

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4th International Conference on Frontiers of Electronics, information and Computation Technologies, ICFEICT 2024

作者： Shen, Jinman Li, Hui Chen, Huamin Wang, Peng Faculty of Information Technology Beijing University of Technology Beijing China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (纸本)9789819653133

Sidelink communication technology is an innovative device-to-device communication method in 5G networks. It is primarily used for vehicle-to-everything (V2X), proximity service, public safety, and so on. 3GPP has been dedicated to developing the technical standards for sidelink. This paper mainly introduces the applications and developments of sidelink technology, and summarizes the detail design of the physical sidelink control channel (PSCCH). In addition, based on the application scenarios and communication requirements of sidelink and traditional wireless communication, the design differences between PSCCH and physical downlink control channel (PDCCH) at the physical layer are illustrated. It also explains the information, application scenarios and functions carried by the sidelink control information 1 (SCI1) transmitted on the PSCCH and the sidelink control information 2 (SCI2) transmitted on the physical sidelink shared channel (PSSCH). Finally, the PSCCH reception algorithm is proposed, and the performance simulation curves of SCI1 and SCI2 under specific parameters are provided, and their performance differences are analyzed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G PDCCH PSCCH SCI1 SCI2 sidelink

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Spatial Analysis of Land Use Land Cover Dynamics in the Madurai District Using Sentinel-2 Data and Supervised Learning Algorithms

Spatial Analysis of Land Use Land Cover Dynamics in the Madu...

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2024 Symposium on Spatial information to Empower the Metaverse

作者： Raguraman, Naren Bhardwaj, Ashutosh International Institute of Information Technology Karnataka Bangalore India Indian Institute of Remote Sensing Uttarakhand Dehradun India

LULC, or Land Use and Land Cover, refers to the classification and description of different types of land and its usage patterns, including urban areas, forests, agricultural land, etc. In remote sensing, satellite imagery for LULC mapping is becoming more widespread. Numerous studies examine various approaches to improve mapping efficiency and accuracy, highlighting the significance of various data sources, machine learning algorithms, and categorization techniques. This study employs machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), Classification and Regression Trees (CART), and K-Nearest Neighbors (KNN) for land use and land cover (LULC) classification of Madurai district utilizing Google Earth Engine. The findings reveal the impressive performance of Random Forest, boasting an overall accuracy of 99.01 percent coupled with a commendable Kappa coefficient of 98.68. Conversely. However, amidst these commendable achievements, it’s noteworthy to highlight the nuanced variations observed between the accuracy of training and validation sets. This discrepancy is attributed to the intrinsic intricacies of the learning processes inherent within the algorithms, underscoring the nuanced nature of algorithmic methodologies and their implications on accuracy assessment within spatial analysis frameworks. The generated land use and land cover (LULC) map allows for a comparison between the ground truth data and the surveys conducted to assess issues such as water scarcity and the drying of natural and man-made water bodies. © Author(s) 2024.

关键词： Support vector machines

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Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

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Digital Communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su Nawaf Alharbe Shuxin Feng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied College Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things Crowdsensing

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Self- and Cross-Attention Enhanced Transformer for Visible and Thermal Infrared Hyperspectral Image Classification

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IEEE Journal of Selected Topics in applied Earth Observations and remote sensing 2025年 18卷 13408-13422页

作者： Zhao, Enyu Su, Yongfang Qu, Nianxin Wang, Yulei Gao, Caixia Zeng, Jian Information Science and Technology College Dalian11-6026 China Chinese Academy of Sciences Key Laboratory of Quantitative Remote Sensing Information Technology Aerospace Information Research Institute Beijing100094 China China Centre for Resources Satellite Data and Application Beijing100094 China

Visible hyperspectral image (V-HSI) and thermal infrared hyperspectral image (TI-HSI) have been crucial data sources for land cover classification. V-HSI can directly provide information of land surface, such as shape, color, texture, and other features. TI-HSI contains rich long-wave spectral information, which can reflect the unique emission characteristics of ground objects in the thermal infrared spectral range. To fully leverage the advantages of V-HSI and TI-HSI while enhancing the classification accuracy, this article proposes a self- and cross-attention enhanced transformer network (SCAET), integrated with convolutional neural network (CNN) for HSI classification. Initially, the proposed method employs a dual-branch spatial-spectral CNN (SS CNN) to extract spectral convolution features from V-HSI and TI-HSI, respectively. Subsequently, a spectral feature mapping (SFM) module is proposed to perform feature transformation, extracting independent and interactive features of V-HSI and TI-HSI. Then, a self- and cross-attention interactive enhancement module is designed to extract deeper features and enhance the independent features by using the interactive features. In addition, a self-projection mixing module is formulated to promote feature interaction and improve the generalization capability of the model. To validate the effectiveness of the proposed network, extensive experiments are conducted on real-world datasets, and the results indicate that SCAET significantly outperforms current multisource fusion networks. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Multi-sensor InSAR time series fusion for long-term land subsidence monitoring

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地球空间信息科学学报（英文版） 2024年第5期27卷中插2,1424-1440页

作者： Haonan Jiang Timo Balz Francesca Cigna Deodato Tapete Jianan Li Yakun Han State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing(LIESMARS)Wuhan UniversityWuhanChina Helmholtz Centre Potsdam GFZ German Research Centre for GeosciencesPotsdamGermany State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing(LIESMARS)Wuhan UniversityWuhanChina Institute of Atmospheric Sciences and Climate(ISAC) National Research Council(CNR)RomaItaly Italian Space Agency(ASI) RomaItaly Institute for Remote Sensing Science and Application School of GeomaticsLiaoning Technical UniversityFuxinChina College of Earth Science Chengdu University of TechnologyChengduChina

Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource ***,most of the existing InSAR studies of ground deformation are based on relatively short periods and single *** paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets,to address cases when partial overlaps and/or temporal gaps exist.A new Power Exponential Knothe Model(PEKM)fits and fuses overlaps in the deformation curves,while a Long Short-Term Memory(LSTM)neural network predicts and fuses any temporal gaps in the *** the city of Wuhan(China)as experiment area,COSMO-SkyMed(2011-2015),TerraSAR-X(2015-2019)and Sentinel-1(2019-2021)SAR datasets were fused to map long-term surface deformation over the last *** independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for *** correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval *** correlation coefficient of the overall results is 0.78,which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation *** experimental results are consistent with existing studies of surface deformation at Wuhan,demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.

关键词： Interferometric Synthetic Aperture Radar(InSAR) Power Exponential Knothe Model(PEKM) Long Short-Term Memory Network(LSTM) data fusion

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