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Artykuły w czasopismach na temat „PolSar dataset”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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6

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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7

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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8

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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9

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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10

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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11

Jiao, Changzhe, Xinlin Wang, Shuiping Gou, et al. "Self-Paced Convolutional Neural Network for PolSAR Images Classification." Remote Sensing 11, no. 4 (2019): 424. http://dx.doi.org/10.3390/rs11040424.

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Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper

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12

Farhadiani, R., S. Homayouni, and A. Safari. "IMPACT OF POLARIMETRIC SAR SPECKLE REDUCTION ON CLASSIFICATION OF AGRICULTURE LANDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 379–85. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-379-2019.

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Abstract. Presence of speckle in the Polarimetric Synthetic Aperture Radar (PolSAR) images could decrease the performance of information extraction applications such as classification, segmentation, change detection, etc. Hence, an essential pre-processing step named de-speckling is needed to suppress this granular noise-like phenomenon from the PolSAR images. In this paper, a comparison study is conducted between several new PolSAR speckle reduction methods such as POSSC, PNGF, and ANLM. For this comparison, a 4-look L-band AIRSAR NASA/JPL PolSAR dataset that obtained over an agriculture land

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13

Karachristos, Konstantinos, Georgia Koukiou, and Vassilis Anastassopoulos. "A Review on PolSAR Decompositions for Feature Extraction." Journal of Imaging 10, no. 4 (2024): 75. http://dx.doi.org/10.3390/jimaging10040075.

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Feature extraction plays a pivotal role in processing remote sensing datasets, especially in the realm of fully polarimetric data. This review investigates a variety of polarimetric decomposition techniques aimed at extracting comprehensive information from polarimetric imagery. These techniques are categorized as coherent and non-coherent methods, depending on their assumptions about the distribution of information among polarimetric cells. The review explores well-established and innovative approaches in polarimetric decomposition within both categories. It begins with a thorough examination

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14

Qiu, Weixing, Zongxu Pan, and Jianwei Yang. "Few-Shot PolSAR Ship Detection Based on Polarimetric Features Selection and Improved Contrastive Self-Supervised Learning." Remote Sensing 15, no. 7 (2023): 1874. http://dx.doi.org/10.3390/rs15071874.

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Deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection over the past few years. However, the backscattering of manmade targets, including ships, is sensitive to the relative geometry between target orientation and radar line of sight, which makes the diversity of polarimetric and spatial features of ships. The diversity of scattering leads to a relative increase in the scarcity of PolSAR-labeled samples, which are difficult to obtain. To solve the abovementioned issue and extract the polarimetric and spatial features of PolS

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15

Gao, Han, Changcheng Wang, Guanya Wang, et al. "A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin." Sensors 18, no. 9 (2018): 3139. http://dx.doi.org/10.3390/s18093139.

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With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix of PolSAR data with the spectral bands of optical data. Using Hoekman’s method, this study solves the above problems by transforming the covariance matrix to an intensity vector that includes multiple intensity values on different polarization basis. In order to reduce the fea

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16

Han, Songli, Dawei Ren, Fan Gao, Jian Yang, and Hui Ma. "ViT–KAN Synergistic Fusion: A Novel Framework for Parameter- Efficient Multi-Band PolSAR Land Cover Classification." Remote Sensing 17, no. 8 (2025): 1470. https://doi.org/10.3390/rs17081470.

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Deep learning has shown significant potential in multi-band Polarimetric Synthetic Aperture Radar (PolSAR) land cover classification. However, the existing methods face two main challenges: accurately modeling the complex nonlinear relationships between multiple bands and balancing classifier parameter efficiency with classification accuracy. To address these challenges, this paper proposes a novel decision-level multi-band fusion framework that leverages the synergistic optimization of the Vision Transformer (ViT) and Kolmogorov–Arnold Network (KAN). This innovative architecture effectively c

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Mohammadimanesh, F., B. Salehi, M. Mahdianpari, and S. Homayouni. "UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 305–10. http://dx.doi.org/10.5194/isprs-archives-xli-b7-305-2016.

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Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of

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Mohammadimanesh, F., B. Salehi, M. Mahdianpari, and S. Homayouni. "UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 305–10. http://dx.doi.org/10.5194/isprsarchives-xli-b7-305-2016.

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Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of

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Chen, Yan, and Zhilong Wang. "Marine Oil Spill Detection from SAR Images Based on Attention U-Net Model Using Polarimetric and Wind Speed Information." International Journal of Environmental Research and Public Health 19, no. 19 (2022): 12315. http://dx.doi.org/10.3390/ijerph191912315.

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With the rapid development of marine trade, marine oil pollution is becoming increasingly severe, which can exert damage to the health of the marine environment. Therefore, detection of marine oil spills is important for effectively starting the oil-spill cleaning process and the protection of the marine environment. The polarimetric synthetic aperture radar (PolSAR) technique has been applied to the detection of marine oil spills in recent years. However, most current studies still focus on using the simple intensity or amplitude information of SAR data and the detection results are not relia

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Zhang, J., J. Zhang, and Z. Zhao. "STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2263–66. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2263-2018.

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Polarimetric Synthetic Aperture Radar（POLSAR） imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, t

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Otgonbaatar, Soronzonbold, and Mihai Datcu. "Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer." Electronics 10, no. 20 (2021): 2482. http://dx.doi.org/10.3390/electronics10202482.

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Satellite instruments monitor the Earth’s surface day and night, and, as a result, the size of Earth observation (EO) data is dramatically increasing. Machine Learning (ML) techniques are employed routinely to analyze and process these big EO data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM poses a quadratic programming problem, and quantum computers including quantum annealers (QA) as well as gate-based quantum computers promise to solve an SVM more efficiently than a conventional computer; training the SVM by employing a quantum computer/conventional computer r

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Song, Guo, Yunkai Deng, Heng Zhang, Xiuqing Liu, and Sheng Chang. "Improving SAR Ship Detection Accuracy by Optimizing Polarization Modes: A Study of Generalized Compact Polarimetry (GCP) Performance." Remote Sensing 17, no. 11 (2025): 1951. https://doi.org/10.3390/rs17111951.

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The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, this study presents the first comprehensive comparison of their ship detection performance against conventional modes using Target-to-Clutter Ratio (TCR) and deep learning-based accuracy (AP50). Experiments on the FPSD dataset reveal that an optimize

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Li, Feng, Chaoqi Zhang, Xin Zhang, and Yang Li. "MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition." Remote Sensing 15, no. 9 (2023): 2292. http://dx.doi.org/10.3390/rs15092292.

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The distinctive polarization information of polarimetric SAR (PolSAR) has been widely applied to terrain classification but is rarely used for PolSAR target recognition. The target recognition strategies built upon multi-feature have gained favor among researchers due to their ability to provide diverse classification information. The paper introduces a robust multi-feature cross-fusion approach, i.e., a multi-feature dual-stage cross manifold attention network, namely, MF-DCMANet, which essentially relies on the complementary information between different features to enhance the representatio

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Khedri, E., M. Hasanlou, and A. Tabatabaeenejad. "ESTIMATING SOIL MOISTURE USING POLSAR DATA: A MACHINE LEARNING APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 133–37. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-133-2017.

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Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil

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Xie, Qinghua, Kunyu Lai, Jinfei Wang, et al. "Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada." Remote Sensing 13, no. 7 (2021): 1394. http://dx.doi.org/10.3390/rs13071394.

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Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observab

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D’Hondt, Olivier, Ronny Hänsch, Nicolas Wagener, and Olaf Hellwich. "Exploiting SAR Tomography for Supervised Land-Cover Classification." Remote Sensing 10, no. 11 (2018): 1742. http://dx.doi.org/10.3390/rs10111742.

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In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extracted from the tomograms. We also provide a comparison of classification results obtained from covariance matrices versus tomogram features as well as obtained by different reference methods, i.e., the trad

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Tan, Weixian, Borong Sun, Chenyu Xiao, Pingping Huang, Wei Xu, and Wen Yang. "A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data." Remote Sensing 13, no. 3 (2021): 355. http://dx.doi.org/10.3390/rs13030355.

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Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land

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Ma, Xiaoshuang, Le Li, and Gang Wang. "Blind Edge-Retention Indicator for Assessing the Quality of Filtered (Pol)SAR Images Based on a Ratio Gradient Operator and Confidence Interval Estimation." Remote Sensing 16, no. 11 (2024): 1992. http://dx.doi.org/10.3390/rs16111992.

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Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on t

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Maiti, A., S. Kumar, V. Tolpekin, and S. Agarwal. "POLARIMETRIC CALIBRATION OF L-BAND AIRBORNE SAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 369–74. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-369-2020.

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Abstract. The PolSAR calibration ensures that the relationship between the SAR observations and the target characteristics on the ground are consistent and resembles the theoretical estimation which in turn improves the overall data quality. Essentially, calibration prevents the propagation of uncertainty into further analysis to characterise the target. In this study, the UAVSAR L-Band data of Rosamond dry lake bed has been calibrated. The calibration of amplitude and phase are carried out with the help of the corner reflector array present in the Rosamond site. The dataset is further calibra

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Zhang, Wangfei, Yongxin Zhang, Yue Yang, and Erxue Chen. "Oilseed Rape (Brassica napus L.) Phenology Estimation by Averaged Stokes-Related Parameters." Remote Sensing 13, no. 14 (2021): 2652. http://dx.doi.org/10.3390/rs13142652.

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Accurate and timely knowledge of crop phenology assists in planning and/or triggering appropriate farming activities. The multiple Polarimetric Synthetic Aperture Radar (PolSAR) technique shows great potential in crop phenology retrieval for its characterizations, such as short revisit time, all-weather monitoring and sensitivity to vegetation structure. This study aims to explore the potential of averaged Stokes-related parameters derived from multiple PolSAR data in oilseed rape phenology identification. In this study, the averaged Stokes-related parameters were first computed by two differe

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Nurmemet, Ilyas, Yang Xiang, Aihepa Aihaiti, et al. "A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery." Agriculture 15, no. 13 (2025): 1376. https://doi.org/10.3390/agriculture15131376.

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Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal d

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Wang, Manlin, Xiaoshuang Ma, Taotao Zheng, and Ziqi Su. "MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images." Sensors 24, no. 21 (2024): 6915. http://dx.doi.org/10.3390/s24216915.

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Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents

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Li, Xiujuan, Yongxin Liu, Pingping Huang, et al. "A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model." Remote Sensing 14, no. 10 (2022): 2441. http://dx.doi.org/10.3390/rs14102441.

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Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in G

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Farhadiani, Ramin, and Saeid Homayouni. "Evaluation of Polarimetric SAR Despeckling Methods for Crop Classification from RCM Compact Polarimetry Data." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-4-2024 (September 12, 2024): 17–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-4-2024-17-2024.

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Abstract. The presence of speckle in RADARSAT Constellation Mission (RCM) Compact Polarimetry (CP) Synthetic Aperture Radar (SAR) images can impair the performance of information extraction applications such as classification. Therefore, a critical preprocessing step known as despeckling is necessary to mitigate this granular, noise-like phenomenon in these images. This paper compared several PolSAR speckle reduction methods, including Box Car, IDAN, Lee Refined, Lee Sigma, Improved Lee Sigma, and Lopez filters. A CP SAR dataset collected over agricultural land in southern Quebec, QC, Canada,

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Reinermann, Sophie, Ursula Gessner, Sarah Asam, Tobias Ullmann, Anne Schucknecht, and Claudia Kuenzer. "Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series." Remote Sensing 14, no. 7 (2022): 1647. http://dx.doi.org/10.3390/rs14071647.

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Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the eco

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Gelas, Colette, Ludovic Villard, Laurent Ferro-Famil, Laurent Polidori, Thierry Koleck, and Sandrine Daniel. "Multi-Temporal Speckle Filtering of Polarimetric P-Band SAR Data over Dense Tropical Forests: Study Case in French Guiana for the BIOMASS Mission." Remote Sensing 13, no. 1 (2021): 142. http://dx.doi.org/10.3390/rs13010142.

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The purpose of this paper is twofold, considering first the generalization of a multichannel speckle filter in order to handle temporal stacks of polarimetric SLC SAR data, and secondly the development of an ad hoc performance indicator based on the Polarimetric Orientation Angle (POA) in order to better estimate the resulting speckle reduction than the standard Equivalent Number of Looks (ENL) over densely vegetated regions, like tropical forests. Being based on the ability of PolSAR measurements to retrieve ground slopes through dense vegetation, this performance indicator requires the use o

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Sofieva, Viktoria F., Monika Szelag, Johanna Tamminen, et al. "Updated merged SAGE-CCI-OMPS+ dataset for the evaluation of ozone trends in the stratosphere." Atmospheric Measurement Techniques 16, no. 7 (2023): 1881–99. http://dx.doi.org/10.5194/amt-16-1881-2023.

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Abstract. In this paper, we present the updated SAGE-CCI-OMPS+ climate data record of monthly zonal mean ozone profiles. This dataset covers the stratosphere and combines measurements by nine limb and occultation satellite instruments – SAGE II (Stratospheric Aerosol and Gases Experiment II), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), GOMOS (Global Ozone Monitoring by Occultation of Stars), ACE-FTS (Atmospheric Chemistry Experiment Fourier T

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Xie, Yanqing, Zhengqiang Li, Weizhen Hou, et al. "Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET." Remote Sensing 13, no. 16 (2021): 3246. http://dx.doi.org/10.3390/rs13163246.

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The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from

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Karlsson, K. G., A. Riihelä, R. Müller, et al. "CLARA-A1: the CM SAF cloud, albedo and radiation dataset from 28 yr of global AVHRR data." Atmospheric Chemistry and Physics Discussions 13, no. 1 (2013): 935–82. http://dx.doi.org/10.5194/acpd-13-935-2013.

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Abstract. A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28-yr period from 1982 until 2009 and consists of cloud, surface albedo and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM

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Karlsson, K. G., A. Riihelä, R. Müller, et al. "CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data." Atmospheric Chemistry and Physics 13, no. 10 (2013): 5351–67. http://dx.doi.org/10.5194/acp-13-5351-2013.

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Abstract. A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (C

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Kvíderová, Jana, Josef Elster, and Ivan Iliev. "Exploitation of databases in polar research - Data evaluation and outputs." Czech Polar Reports 5, no. 2 (2015): 143–59. http://dx.doi.org/10.5817/cpr2015-2-13.

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The increasing number of observations and floristic sample analyses provided by the Centre for Polar Ecology, Faculty of Science, University of South Bohemia in České Budějovice, Czech Republic (CPE), led to development of the sample database (SampleDTB). At present, the Sample DTB contains records on total of 318 samples from 135 sites. Total of 254 taxa at level of genera or species were observed. For database functionality tests, two datasets were selected. The first one consisted of samples collected by ALGO groups in frame of Polar Ecology course organized by the CPE in 2011-2014 (ALGO da

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G S, Na, Li R J, and Lu Z H. "Dechlorane Dataset in Polar Regions (2012-2014)." Journal of Global Change Data & Discovery 1, no. 1 (2017): 74–79. http://dx.doi.org/10.3974/geodp.2017.01.11.

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Ampong, Isaac, Kip D. Zimmerman, Peter W. Nathanielsz, Laura A. Cox, and Michael Olivier. "Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data." Metabolites 12, no. 5 (2022): 429. http://dx.doi.org/10.3390/metabo12050429.

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Gas chromatography–coupled mass spectrometry (GC–MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC–MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonst

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Yun, Xiang, Boyin Huang, Jiayi Cheng, Wenhui Xu, Shaobo Qiao, and Qingxiang Li. "A new merge of global surface temperature datasets since the start of the 20th century." Earth System Science Data 11, no. 4 (2019): 1629–43. http://dx.doi.org/10.5194/essd-11-1629-2019.

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Abstract. Global surface temperature (ST) datasets are the foundation for global climate change research. Several global ST datasets have been developed by different groups in NOAA NCEI, NASA GISS, UK Met Office Hadley Centre &amp; UEA CRU, and Berkeley Earth. In this study, a new global ST dataset named China Merged Surface Temperature (CMST) was presented. CMST is created by merging the China-Land Surface Air Temperature (C-LSAT1.3) with sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The merge of C-LSAT and ERSSTv5 shows a

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Town, Michael S., Von P. Walden, and Stephen G. Warren. "Cloud Cover over the South Pole from Visual Observations, Satellite Retrievals, and Surface-Based Infrared Radiation Measurements." Journal of Climate 20, no. 3 (2007): 544–59. http://dx.doi.org/10.1175/jcli4005.1.

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Abstract Estimates of cloud cover over the South Pole are presented from five different data sources: routine visual observations (1957–2004; Cvis), surface-based spectral infrared (IR) data (2001; CPAERI), surface-based broadband IR data (1994–2003; Cpyr), the Extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) dataset (1994–99; CAPP-x), and the International Satellite Cloud Climatology Project (ISCCP) dataset (1994–2003; CISCCP). The seasonal cycle of cloud cover is found to range from 45%–50% during the short summer to a relatively constant 55%–65% during the

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Zuerl, Matthias, Richard Dirauf, Franz Koeferl, et al. "PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears." Animals 13, no. 5 (2023): 801. http://dx.doi.org/10.3390/ani13050801.

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Automated monitoring systems have become increasingly important for zoological institutions in the study of their animals’ behavior. One crucial processing step for such a system is the re-identification of individuals when using multiple cameras. Deep learning approaches have become the standard methodology for this task. Especially video-based methods promise to achieve a good performance in re-identification, as they can leverage the movement of an animal as an additional feature. This is especially important for applications in zoos, where one has to overcome specific challenges such as ch

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Lanconelli, C., M. Busetto, E. G. Dutton, et al. "Polar baseline surface radiation measurements during the International Polar Year 2007–2009." Earth System Science Data 3, no. 1 (2011): 1–8. http://dx.doi.org/10.5194/essd-3-1-2011.

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Abstract. Downwelling and upwelling shortwave and longwave radiation components from six active polar sites, taking part of the Baseline Surface Radiation Network (BSRN), were selected for the period of the last International Polar Year (March 2007 to March 2009), and included in the BSRN-IPY dataset, along with metadata and supplementary data for some of the stations. Two sites, located at Svalbard archipelago (Ny Ålesund) and Alaska (Barrow), represent Arctic sea-level conditions. Four Antarctic stations represent both sea-level (Dronning Maud Land and Cosmonaut Sea) and high-elevation condi

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Mayol, Eduardo, Mercedes Campillo, Arnau Cordomí, and Mireia Olivella. "Inter-residue interactions in alpha-helical transmembrane proteins." Bioinformatics 35, no. 15 (2018): 2578–84. http://dx.doi.org/10.1093/bioinformatics/bty978.

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Abstract Motivation The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in α-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date. Results Our analysis of residue–residue interactions in TM segments of membrane proteins shows that almost all interactions involve aliphatic residues and Phe. There is lack of polar–polar, po

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Blesić, Suzana, Davide Zanchettin, and Angelo Rubino. "Heterogeneity of Scaling of the Observed Global Temperature Data." Journal of Climate 32, no. 2 (2018): 349–67. http://dx.doi.org/10.1175/jcli-d-17-0823.1.

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Abstract We investigated the scaling properties of two datasets of the observed near-surface global temperature data anomalies: the Met Office and the University of East Anglia Climatic Research Unit HadCRUT4 dataset and the NASA GISS Land–Ocean Temperature Index (LOTI) dataset. We used detrended fluctuation analysis of second-order (DFA2) and wavelet-based spectral (WTS) analysis to investigate and quantify the global pattern of scaling in two datasets and to better understand cyclic behavior as a possible underlying cause of the observed forms of scaling. We found that, excluding polar and p

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Lanconelli, C., M. Busetto, E. G. Dutton, et al. "Polar baseline surface radiation measurements during the International Polar Year 2007–2009." Earth System Science Data Discussions 3, no. 2 (2010): 259–79. http://dx.doi.org/10.5194/essdd-3-259-2010.

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Abstract. Downwelling and upwelling shortwave and longwave radiation components from six active polar sites, taking part of the Baseline Surface Radiation Network (BSRN), were selected for the period of the last International Polar Year (March 2007 to March 2009), and included in the BSRN-IPY dataset, along with metadata and supplementary data for some of the stations. Two sites, located at Svalbard archipelago (Ny Ålesund) and Alaska (Barrow), represent Arctic sea-level conditions. Four Antarctic stations represent both sea-level (Dronning Maud Land and Cosmonaut Sea) and high-elevation condi

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