Relevant bibliographies by topics / Spectral-semantic model

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Spectral-semantic model'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Spectral-semantic model"

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Liu, Dongxu, Qingqing Li, Meihui Li, and Jianlin Zhang. "A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification." Remote Sensing 15, no. 18 (2023): 4642. http://dx.doi.org/10.3390/rs15184642.

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Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic
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Chen, Yuhan, Qingyun Yan, and Weimin Huang. "MSSFF: Advancing Hyperspectral Classification through Higher-Accuracy Multistage Spectral–Spatial Feature Fusion." Remote Sensing 15, no. 24 (2023): 5717. http://dx.doi.org/10.3390/rs15245717.

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This paper presents the MSSFF (multistage spectral–spatial feature fusion) framework, which introduces a novel approach for semantic segmentation from hyperspectral imagery (HSI). The framework aims to simplify the modeling of spectral relationships in HSI sequences and unify the architecture for semantic segmentation of HSIs. It incorporates a spectral–spatial feature fusion module and a multi-attention mechanism to efficiently extract hyperspectral features. The MSSFF framework reevaluates the potential impact of spectral and spatial features on segmentation models and leverages the spectral
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Shen, Feiyu. "Research on the Application of Computer Big Data Technology in English Online Translation." Highlights in Science, Engineering and Technology 68 (October 9, 2023): 351–56. http://dx.doi.org/10.54097/hset.v68i.12499.

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Aiming at the disadvantages of the traditional rule-based machine translation model that the English translation results are not accurate enough and it is difficult to accurately describe the relationship between words, the English machine translation model based on the semantic network is designed and improved. The algorithm analyses the English grammatical rules, then performs Gaussian marginalization on the semantics to obtain the rectangular window function, obtains the window feature vector, projects the semantic information entropy data, and adds the semantic correlation factors to the i
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Zhu, Qiqi, Yanfei Zhong, and Liangpei Zhang. "SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 451–57. http://dx.doi.org/10.5194/isprs-archives-xli-b7-451-2016.

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Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene clas
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Zhu, Qiqi, Yanfei Zhong, and Liangpei Zhang. "SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 451–57. http://dx.doi.org/10.5194/isprsarchives-xli-b7-451-2016.

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Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene clas
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Guo, Yu Tang, and Chang Gang Han. "Automatic Image Annotation Using Semantic Subspace Graph Spectral Clustering Algorithm." Advanced Materials Research 271-273 (July 2011): 1090–95. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1090.

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Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic simila
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Pi, Weiqiang, Tao Zhang, Rongyang Wang, Guowei Ma, Yong Wang, and Jianmin Du. "Semantic-Guided Transformer Network for Crop Classification in Hyperspectral Images." Journal of Imaging 11, no. 2 (2025): 37. https://doi.org/10.3390/jimaging11020037.

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The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitations when processing hyperspectral remote sensing images, primarily in the following aspects. First, the complex background in the images. Various elements in the background may have similar spectral characteristics to the crops, and this spectral similarity makes the classification model susceptible to background interference, thus reducing classi
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Wu, Hao, Canhai Li, and Yongchang Li. "Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 267–74. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-267-2024.

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Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification.
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Du, Zhen, Senhao Liu, Yao Liao, et al. "UniHSFormer X for Hyperspectral Crop Classification with Prototype-Routed Semantic Structuring." Agriculture 15, no. 13 (2025): 1427. https://doi.org/10.3390/agriculture15131427.

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Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling
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Liu, Haijun, Fenglei Chen, Zhihong Zeng, and Xiaoheng Tan. "AMFuse: Add–Multiply-Based Cross-Modal Fusion Network for Multi-Spectral Semantic Segmentation." Remote Sensing 14, no. 14 (2022): 3368. http://dx.doi.org/10.3390/rs14143368.

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Multi-spectral semantic segmentation has shown great advantages under poor illumination conditions, especially for remote scene understanding of autonomous vehicles, since the thermal image can provide complementary information for RGB image. However, methods to fuse the information from RGB image and thermal image are still under-explored. In this paper, we propose a simple but effective module, add–multiply fusion (AMFuse) for RGB and thermal information fusion, consisting of two simple math operations—addition and multiplication. The addition operation focuses on extracting cross-modal comp
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Dissertations / Theses on the topic "Spectral-semantic model"

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Солонская, Светлана Владимировна. "Модели, метод и информационная технология обработки сигналов в интеллектуальных радиолокационных комплексах". Thesis, Харьковский национальный университет радиоэлектроники, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/23588.

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Диссертация на соискание ученой степени кандидата технических наук по специальности 05.13.06 – информационные технологии. – Национальный технический университет "Харьковский политехнический институт", Харьков, 2016. Диссертация посвящена решению научно-практической задачи разработки метода для повышения эффективности обнаружения и распознавания сигналов в радиолокационных комплексах путем интеллектуализации обработки сигнальной информации. В работе проанализированы научные достижения в области обработки сигналов, определены задачи обработки сигналов и подходы к их решению. В технологии обрабо
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Солонська, Світлана Володимирівна. "Моделі, метод та інформаційна технологія обробки сигналів в інтелектуальних радіолокаційних комплексах". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/23586.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016. У дисертаційній роботі вирішена науково-практична задача розроблення методу для підвищення ефективності виявлення та розпізнавання сигналів в радіолокаційних комплексах шляхом інтелектуалізації обробки сигнальної інформації. У роботі проаналізовано наукові досягнення в галузі обробки сигналів, визначено задачі обробки сигналів та підходи до їх вирішення. У технології обробки радіолокаційн
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Лавриненко, Олександр Юрійович, Александр Юрьевич Лавриненко та Oleksandr Lavrynenko. "Методи підвищення ефективності семантичного кодування мовних сигналів". Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/52212.

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Дисертаційна робота присвячена вирішенню актуальної науково-практичної проблеми в телекомунікаційних системах, а саме підвищення пропускної здатності каналу передачі семантичних мовних даних за рахунок ефективного їх кодування, тобто формулюється питання підвищення ефективності семантичного кодування, а саме – з якою мінімальною швидкістю можливо кодувати семантичні ознаки мовних сигналів із заданою ймовірністю безпомилкового їх розпізнавання? Саме на це питання буде дана відповідь у даному науковому дослідженні, що є актуальною науково-технічною задачею враховуючи зростаючу тенденцію д
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Najafi, Mohammad. "On the Role of Context at Different Scales in Scene Parsing." Phd thesis, 2017. http://hdl.handle.net/1885/116302.

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Scene parsing can be formulated as a labeling problem where each visual data element, e.g., each pixel of an image or each 3D point in a point cloud, is assigned a semantic class label. One can approach this problem by training a classifier and predicting a class label for the data elements purely based on their local properties. This approach, however, does not take into account any kind of contextual information between different elements in the image or point cloud. For example, in an application where we are interested in labeling roadside objects, t
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Book chapters on the topic "Spectral-semantic model"

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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cu
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Butterworth James and Dunne Paul E. "Spectral Techniques in Argumentation Framework Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-686-6-167.

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Spectral analysis – the study of the properties of the eigenvalues associated with some matrix derived from an underlying graph form – has proven to offer valuable insights in many domains where graph-theoretic models are prevalent. Abstract argumentation frameworks (afs) are, of course, one such model and have provided a unifying basis for defining semantic properties related to concepts of “argument acceptability”. In this paper we consider the possible benefits of adopting spectral methods as a tool for analysing argumentation structures, presenting a
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Deeb, Bashar M., Andrey Savchenko, and Ilya Makarov. "CA-SER: Cross-Attention Feature Fusion for Speech Emotion Recognition." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241034.

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In this paper, we introduce a novel tool for speech emotion recognition, CA-SER, that borrows self-supervised learning to extract semantic speech representations from a pre-trained wav2vec 2.0 model and combine them with spectral audio features to improve speech emotion recognition. Our approach involves a self-attention encoder on MFCC features to capture meaningful patterns in audio sequences. These MFCC features are combined with high-level representations using a multi-head cross-attention mechanism. Evaluation of speech emotion recognition on the IEMOCAP dataset shows that our system achi
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Kiyoki Yasushi, Chen Xing, Sasaki Shiori, and Koopipat Chawan. "A Globally-Integrated Environmental Analysis and Visualization System with Multi-Spectral & Semantic Computing in “Multi-Dimensional World Map”." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-720-7-106.

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In the design of multimedia data mining systems, one of the most important issues is how to search and analyze media data, according to contexts. We have introduced a semantic associative search method based on our “Mathematical Model of Meaning (MMM) [1, 2, 3]”. This model is applied to compute semantic correlations between keywords, images, music and documents dynamically in a context-dependent way.
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Pashkovska, Liudmila. "INNOVATIVE VIOLIN METHOD OF TEACHING AS A MECHANISM FOR FORMING THE MEANING FORMATION OF MUSICAL NARRATIVE OF VIOLIN INSTRUMENTAL MUSIC OF THE ERA ON ROMANTICISM (ON THE EXAMPLE OF 5-TH CAPRICE OF PAGANINI’S FROM THE CYCLE “24 CAPRICES FOR SOLO VIOLIN”)." In Integration of traditional and innovation processes of development of modern science. Publishing House “Baltija Publishing”, 2020. http://dx.doi.org/10.30525/978-9934-26-021-6-14.

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This article is devoted to the innovative violin method of teaching which reveals different angles of the paradigm processes of the formation of the text of violin instrumental music of the era on Romanticism and the semantic organization of the musical narrative in the development of the intertextual dialogue "one's own-another's" on the material of the 5th Caprice for Paganini's violin-solo. The problem of dialogue in art in a broad sense is now one of the most popular both in modern domestic musicology and in culture in general. This exploration constitutes a fragmented but very important s
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Conference papers on the topic "Spectral-semantic model"

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Luperto, Matteo, Leone D'Emilio, and Francesco Amigoni. "A generative spectral model for semantic mapping of buildings." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7354009.

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Shubin, Igor, Svitlana Solonska, Stanislav Snisar, Volodymyr Zhyrnov, Vlad Slavhorodskyi, and Victoria Skovorodnikova. "Efficiency Evaluation for Radar Signal Processing on the Basis of Spectral-Semantic Model." In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2020. http://dx.doi.org/10.1109/tcset49122.2020.235416.

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Luo, Junyu, Zhiping Xiao, Yifan Wang, et al. "Rank and Align: Towards Effective Source-free Graph Domain Adaptation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/520.

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Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inhar
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Feldmann, Carolin, Thomas Carolus, and Marc Schneider. "A Semantic Differential for Evaluating the Sound Quality of Fan Systems." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63172.

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Fans are main components e.g. in heating, ventilating and air conditioning systems for vehicles or buildings, cooling units of engines and electronic circuits, and household appliances such as kitchen exhaust hoods or vacuum cleaners. End-users increasingly demand a high sound quality of their system or device. The overall objective of a recent research project at the University of Siegen is a multidimensional assessment of fan sound quality. In a first step an advanced novel semantic differential for the assessment of fan-related sounds is established with the aid of carefully designed jury t
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