Relevant bibliographies by topics / PolSar dataset

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Academic literature on the topic 'PolSar dataset'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "PolSar dataset"

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Wang, Yuxi, Wenjuan Zhang, Jie Pan, et al. "AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR." Remote Sensing 17, no. 2 (2025): 275. https://doi.org/10.3390/rs17020275.

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Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical image
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Wang, Yangyang, Wengang Zhang, Weidong Chen, and Chang Chen. "BSDSNet: Dual-Stream Feature Extraction Network Based on Segment Anything Model for Synthetic Aperture Radar Land Cover Classification." Remote Sensing 16, no. 7 (2024): 1150. http://dx.doi.org/10.3390/rs16071150.

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Land cover classification using high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images obtained from satellites is a challenging task. While deep learning algorithms have been extensively studied for PolSAR image land cover classification, the performance is severely constrained due to the scarcity of labeled PolSAR samples and the limited domain acceptance of models. Recently, the emergence of the Segment Anything Model (SAM) based on the vision transformer (VIT) model has brought about a revolution in the study of specific downstream tasks in computer vision. Benefiting from i
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Wang, Hongmiao, Cheng Xing, Junjun Yin, and Jian Yang. "Land Cover Classification for Polarimetric SAR Images Based on Vision Transformer." Remote Sensing 14, no. 18 (2022): 4656. http://dx.doi.org/10.3390/rs14184656.

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Deep learning methods have been widely studied for Polarimetric synthetic aperture radar (PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small receptive field of the model limit the performance of deep learning methods for land cover classification. In this paper, a vision Transformer (ViT)-based classification method is proposed. The ViT structure can extract features from the global range of images based on a self-attention block. The powerful feature representation capability of the model is equivalent to a flexible receptive field, which is suitable for P
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Ren, Yihui, Wen Jiang, and Ying Liu. "A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification." Remote Sensing 15, no. 19 (2023): 4801. http://dx.doi.org/10.3390/rs15194801.

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Polarimetric synthetic aperture radar (PolSAR) image classification has been an important area of research due to its wide range of applications. Traditional machine learning methods were insufficient in achieving satisfactory results before the advent of deep learning. Results have significantly improved with the widespread use of deep learning in PolSAR image classification. However, the challenge of reconciling the complex-valued inputs of PolSAR images with the real-valued models of deep learning remains unsolved. Current complex-valued deep learning models treat complex numbers as two dis
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Zhu, Lekun, Xiaoshuang Ma, Penghai Wu, and Jiangong Xu. "Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method." Sensors 21, no. 9 (2021): 3006. http://dx.doi.org/10.3390/s21093006.

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Polarimetric synthetic aperture radar (PolSAR) image classification has played an important role in PolSAR data application. Deep learning has achieved great success in PolSAR image classification over the past years. However, when the labeled training dataset is insufficient, the classification results are usually unsatisfactory. Furthermore, the deep learning approach is based on hierarchical features, which is an approach that cannot take full advantage of the scattering characteristics in PolSAR data. Hence, it is worthwhile to make full use of scattering characteristics to obtain a high c
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Shao, Qiqi, Lingjuan Yu, Yuting Guo, Xiaochun Xie, Jianping Zou, and Liang Li. "Weakly Supervised Semantic Segmentation of PolSAR Image Based on Improved SEAM." Journal of Physics: Conference Series 2456, no. 1 (2023): 012003. http://dx.doi.org/10.1088/1742-6596/2456/1/012003.

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Abstract Weakly supervised semantic segmentation (WSSS) has been widely studied in optical image field. Self-supervised equivariant attention mechanism (SEAM) effectively improves the WSSS results with the image-level labels. However, when it is directly used in the WSSS of polarimetric synthetic aperture radar (PolSAR) image, the performance is very poor. In this paper, an improved SEAM (ISEAM) is proposed for WSSS of PolSAR image, which uses the improved ResNet as the backbone network. The improvement mainly includes two aspects. First, the structure of ResNet is lightweight, which aims to m
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Radman, Ali, Masoud Mahdianpari, Brian Brisco, Bahram Salehi, and Fariba Mohammadimanesh. "Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification." Remote Sensing 15, no. 1 (2022): 75. http://dx.doi.org/10.3390/rs15010075.

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Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast to optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods can provide solutions to address PolSAR feature extraction challenges. The convolutional neural networks (CNNs) and graph convolutional networks (GCNs) can drive PolSAR image characteristics by deploying kernel abilities in considering neighborhood (local) information and graphs in conside
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Wu, Guoqing, Shengbin Luo Wang, Yibin Liu, Ping Wang, and Yongzhen Li. "Ship Contour Extraction from Polarimetric SAR Images Based on Polarization Modulation." Remote Sensing 16, no. 19 (2024): 3669. http://dx.doi.org/10.3390/rs16193669.

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Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. T
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Qiu, Weixing, and Zongxu Pan. "Polarimetric Synthetic Aperture Radar Ship Potential Area Extraction Based on Neighborhood Semantic Differences of the Latent Dirichlet Allocation Bag-of-Words Topic Model." Remote Sensing 15, no. 23 (2023): 5601. http://dx.doi.org/10.3390/rs15235601.

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Recently, deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection. However, extracting polarimetric and spatial features on the whole PolSAR image will result in high computational complexity. In addition, in the massive data ship detection task, the image to be detected contains a large number of invalid areas, such as land and seawater without ships. Therefore, using ship coarse detection methods to quickly locate the potential areas of ships, that is, ship potential area extraction, is an important prerequisite for PolSAR
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Yu, Lingjuan, Qiqi Shao, Yuting Guo, Xiaochun Xie, Miaomiao Liang, and Wen Hong. "Complex-Valued U-Net with Capsule Embedded for Semantic Segmentation of PolSAR Image." Remote Sensing 15, no. 5 (2023): 1371. http://dx.doi.org/10.3390/rs15051371.

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In recent years, semantic segmentation with pixel-level classification has become one of the types of research focus in the field of polarimetric synthetic aperture radar (PolSAR) image interpretation. Fully convolutional network (FCN) can achieve end-to-end semantic segmentation, which provides a basic framework for subsequent improved networks. As a classic FCN-based network, U-Net has been applied to semantic segmentation of remote sensing images. Although good segmentation results have been obtained, scalar neurons have made it difficult for the network to obtain multiple properties of ent
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Dissertations / Theses on the topic "PolSar dataset"

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Roizman, Violeta. "Flexible clustering algorithms for heterogeneous datasets." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG002.

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L'objectif de la segmentation de données ou clustering est de trouver des groupes homogènes en fonction d'une distance prédeterminée. Étant donnée sa nature non supervisée, le clustering peut être appliqué à tout type de données et peut s'affranchir de processus d'étiquetage (labels) qui peuvent s'avérer très coûteux. Parmi les algorithmes de clustering les plus populaires, celui basé sur le modèle de mélange gaussien (MMG) est particulièrement intéressant. En effet, cet algorithme est très intuitif et fonctionne très bien lorsque les groupes ont une forme elliptique.Cependant, le modèle MMG e
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Book chapters on the topic "PolSar dataset"

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Liu, Xu, Licheng Jiao, Fang Liu, Dan Zhang, and Xu Tang. "PolSF: PolSAR Image Datasets on San Francisco." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14903-0_23.

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Hastings, Jordan Towner. "Digital Geospatial Datasets Pertaining to the Mcmurdo Dry Valleys of Antarctica: The Sola/Agu Cdrom." In Ecosystem Dynamics in a Polar Desert: the Mcmurdo Dry Valleys, Antarctica. American Geophysical Union, 2013. http://dx.doi.org/10.1029/ar072p0365.

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Lenz, Mirko, and Ralph Bergmann. "PolArg: Unsupervised Polarity Prediction of Arguments in Real-Time Online Conversations." In Robust Argumentation Machines. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63536-6_7.

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AbstractThe increasing usage of social networks has led to a growing number of discussions on the Internet that are a valuable source of argumentation that occurs in real time. Such conversations are often made up of a large number of participants and are characterized by a fast pace. Platforms like X/Twitter and Hacker News (HN) allow users to respond to other users’ posts, leading to a tree-like structure. Previous work focused on training supervised models on datasets obtained from debate portals like Kialo where authors provide polarity labels (i.e., support/attack) together with their pos
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Terres-Escudero, Erik B., Javier Del Ser, and Pablo Garcia-Bringas. "On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240706.

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Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional contrastive forward pass. Among these approaches, the so-called Forward-Forward Algorithm (FFA) has been shown to achieve competitive levels of performance in terms of generalization and complexity. Networks trained using FFA learn to contrastively maximize a layer-wise defined goodness score when presented with real data (denoted as positive samples) and to minimize it when processing synthetic data (corr. negative sa
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Jung, Oliver, Mathias Schmoigl-Tonis, Christoph Schranz, et al. "A Learning Agent for Stress Multi-Level Diagnostics, Personalised Stress Profiles and Interventions in the Work Context." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250196.

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Background: Work-related stress affects 39% of Austrians, contributing to mental health issues like depression and burnout, driven by factors such as workload and lack of control. Objectives: The Relax project aims to develop a holistic stress management framework using continuous stress assessment and personalized interventions based on physiological, behavioral, emotional, and cognitive indicators. Methods: The study combines wearable sensors (e.g., Polar Verity Sense), psychological methods, and technical strategies, with a longitudinal design to assess the app’s usability and effectiveness
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Yong, Fang, Zhang Li, Gong Hui, Cao Bincai, Gao Li, and Hu Haiyan. "Spaceborne LiDAR Surveying and Mapping." In LiDAR Technology - From Surveying to Digital Twins [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108177.

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Laser point cloud data have the characteristics of high elevation accuracy, fast processing efficiency, strong three-dimensional (3D) vision, and wide application fields. It will be one of the core datasets of the new generation national global topographic database. The rapid advancement of spaceborne laser earth observation technology allows the collection of global 3D point cloud data, which has brought a new breakthrough in the field of satellite-based earth observation, and its significant advantages of all-day time, high accuracy and high efficiency will lead the future development of spa
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Cui, Taoyong, Jianze Li, Yuhan Dong, and Li Liu. "TAOTF: A Two-Stage Approximately Orthogonal Training Framework in Deep Neural Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230310.

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The orthogonality constraints, including the hard and soft ones, have been used to normalize the weight matrices of Deep Neural Network (DNN) models, especially the Convolutional Neural Network (CNN) and Vision Transformer (ViT), to reduce model parameter redundancy and improve training stability. However, the robustness to noisy data of these models with constraints is not always satisfactory. In this work, we propose a novel two-stage approximately orthogonal training framework (TAOTF) to find a trade-off between the orthogonal solution space and the main task solution space to solve this pr
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Conference papers on the topic "PolSar dataset"

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Ma, Wentao, and Shuang Liang. "POLAR: Posture-level Action Recognition Dataset." In 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019. http://dx.doi.org/10.1109/icsai48974.2019.9010160.

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Brigot, Guillaume, Marc Simard, Elise Koeniguer, and Cedric Taillandier. "Prediction of forest canopy structure from PolInSAR dataset." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127954.

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Kvamme, Bjarte O., Adekunle P. Orimolade, Sverre K. Haver, and Ove T. Gudmestad. "Marine Operation Windows Offshore Norway." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54840.

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A study of the wave conditions in the North Sea, the Norwegian Sea and the Barents Sea is presented in this paper. For each region, one reference location for which there are buoy measurements is selected. For the selected locations, WAM10 hindcast data are obtained from the Norwegian Meteorological Institute (MET Norway). The hindcast data for each location cover the period from 1957 to 2014. First, the hindcast datasets were validated against available buoy measurements — both for extreme value predictions and for application of hindcast data for planning of marine operations. The validation
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Burgess, Ann B., and Chris A. Mattmann. "Automatically classifying and interpreting polar datasets with Apache Tika." In 2014 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2014. http://dx.doi.org/10.1109/iri.2014.7051982.

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Ning, Ke, Lingxi Xie, Fei Wu, and Qi Tian. "Polar Relative Positional Encoding for Video-Language Segmentation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/132.

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In this paper, we tackle a challenging task named video-language segmentation. Given a video and a sentence in natural language, the goal is to segment the object or actor described by the sentence in video frames. To accurately denote a target object, the given sentence usually refers to multiple attributes, such as nearby objects with spatial relations, etc. In this paper, we propose a novel Polar Relative Positional Encoding (PRPE) mechanism that represents spatial relations in a ``linguistic'' way, i.e., in terms of direction and range. Sentence feature can interact with positional embeddi
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Liang, Shuang, Jiangyuan Zeng, Zhen Li, Kun-Shan Chen, Ping Zhang, and Haiyun Bi. "Comparison of Remotely Sensed Sea Ice Concentrations with Reanalysis Dataset in Polar Regions." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8899166.

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Gebhard, Lukas, and Felix Hamborg. "The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity." In JCDL '20: The ACM/IEEE Joint Conference on Digital Libraries in 2020. ACM, 2020. http://dx.doi.org/10.1145/3383583.3398567.

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Suprapty, Bedi, Anggri Sartika Wiguna, Agusma Wajiansyah, and Rheo Malani. "Dataset transformation using hybrid method of polar-based cartesian and image filtering technique for annual rainfall clustering." In 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2021. http://dx.doi.org/10.1109/isitia52817.2021.9502202.

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Molan, Nika, Ema Leila Grošelj, and Klemen Vovk. "A Bayesian Approach to Modeling GPS Errors for Comparing Forensic Evidence." In 10th Student Computing Research Symposium. University of Maribor Press, 2024. https://doi.org/10.18690/um.feri.6.2024.10.

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This paper introduces a Bayesian approach to modeling GPS er-rors for comparing forensic evidence, addressing the challenge of determining the most likely source of a single GPS localization given two proposed locations. We develop a probabilistic model that transforms GPS coordinates into polar coordinates, capturing distance and directional errors. Our method employs Markov chain Monte Carlo (MCMC) sampling to estimate the data-generating processes of GPS measurements, enabling robust comparison of potential device locations while quantifying uncertainty. We apply this approach to three data
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Price, G., and C. Moore. "A simple polar parameterisation method to show quantitative spatial variation between delineations on medical image datasets." In Proceedings. Third International Conference on Medical Information Visualisation. IEEE, 2005. http://dx.doi.org/10.1109/medivis.2005.4.

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Reports on the topic "PolSar dataset"

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LeFebvre, Rebecca. Implementing Undergraduate Research in an Online Gateway Political Science Course (Dataset). Kennesaw State University, 2020. http://dx.doi.org/10.32727/27.2022.1.

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Introductory political science courses are usually considered Gateway courses to student success in college, yet those courses often use minimal high impact practices. This study investigates a Course-based Undergraduate Research Experience (CURE) as a means to increase students’ self-assessed learning gains and motivation to acquire critical thinking skills. This study used a quasi-experiment across two online sections of POLS 1101, American Government, taught at a large public Southeastern university. The experimental section made use of a CURE, and the control section did not. Pre- and post
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Lever, James, Allan Delaney, Laura Ray, E. Trautman, Lynette Barna, and Amy Burzynski. Autonomous GPR surveys using the polar rover Yeti. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/43600.

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The National Science Foundation operates stations on the ice sheets of Antarctica and Greenland to investigate Earth’s climate history, life in extreme environments, and the evolution of the cosmos. Understandably, logistics costs predominate budgets due to the remote locations and harsh environments involved. Currently, manual ground-penetrating radar (GPR) surveys must preceed vehicle travel across polar ice sheets to detect subsurface crevasses or other voids. This exposes the crew to the risks of undetected hazards. We have developed an autonomous rover, Yeti, specifically to conduct GPR s
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