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IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium最新文献

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Multi-Function Radar Work Mode Recognition Based on Encoder-Decoder Model 基于编码器-解码器模型的多功能雷达工作模式识别
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884556
Hongyu Chen, Kangan Feng, Yukai Kong, Lidong Zhang, Xianxiang Yu, Wei Yi
{"title":"Multi-Function Radar Work Mode Recognition Based on Encoder-Decoder Model","authors":"Hongyu Chen, Kangan Feng, Yukai Kong, Lidong Zhang, Xianxiang Yu, Wei Yi","doi":"10.1109/IGARSS46834.2022.9884556","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884556","url":null,"abstract":"The multi-function radar (MFR) is capable of transmitting complex signals and achieving beam agility according to different tasks, thus leading to many challenges in the work mode recognition field. This paper, therefore, develops an Encoder-Decoder model based on the gated recurrent units (GRU) network to achieve work mode recognition. It involves the use of the Encoder structure to extract the temporal features and the work mode transition regulations of the intercepted pulse group sequence while leveraging the Decoder structure to decode the features and transition regulations as a part of the input for the next decoding process. Additionally, the label substitution (LS) method is utilized for improving the ability of the Decoder structure to recognize work mode under non-ideal situations. Simulation results show that the proposed method is less sensitive in the presence of non-ideal situations and shares better performance than existing work mode recognition methods.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129305787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Microwave Electrojet Magnetogram (MEM) Instrument for the Electrojet Zeeman Imaging Explorer (EZIE) Mission 用于电喷塞曼成像探测器(EZIE)任务的微波电喷磁图(MEM)仪器
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884004
S. Padmanabhan, S. Misra, P. Kangaslahti, O. Montes, Javier Bosch-Luis, R. Cofield, I. Ramos, Sam Yee
{"title":"Microwave Electrojet Magnetogram (MEM) Instrument for the Electrojet Zeeman Imaging Explorer (EZIE) Mission","authors":"S. Padmanabhan, S. Misra, P. Kangaslahti, O. Montes, Javier Bosch-Luis, R. Cofield, I. Ramos, Sam Yee","doi":"10.1109/IGARSS46834.2022.9884004","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884004","url":null,"abstract":"The Electrojet Zeeman Imaging Explorer (EZIE) is an innovative multi-satellite mission that images the magnetic fingerprint of intense electrical currents flowing in the upper layers of Earth's atmosphere. EZIE's multi-point measurements of these electrojets will provide closure to decades-old, and much debated, mysteries of the interaction between the Earth and the surrounding space. Each of EZIE's three satellites carries a microwave electrojet magnetogram (MEM) instrument which consists of four identical 118-GHz heterodyne spectropolarimeters. They are designed and optimized to cost-effectively meet EZIE's measurement requirements. EZIE's MEM instruments use the Zeeman effect to infer magnetic fields at ~80 km altitude. The technique has been used extensively to derive the Sun's magnetic field. EZIE now applies this technique to the Earth system.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129339145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
EOD: The IEEE GRSS Earth Observation Database ieeegrss地球观测数据库
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884725
M. Schmitt, Pedram Ghamisi, N. Yokoya, Ronny Hansch
{"title":"EOD: The IEEE GRSS Earth Observation Database","authors":"M. Schmitt, Pedram Ghamisi, N. Yokoya, Ronny Hansch","doi":"10.1109/IGARSS46834.2022.9884725","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884725","url":null,"abstract":"In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, each designed for a specific data type and with a specific task or application in mind. In the jungle of remote sensing datasets, it can be hard to keep track of what is available already. With this paper, we introduce EOD - the IEEE GRSS Earth Observation Database (EOD) - an interactive online platform for cataloguing different types of datasets leveraging remote sensing imagery.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
A Literature Systematic Review of Thermal Infrared Remote Sensing Satellites Land Surface Temperature 热红外遥感卫星地表温度研究文献综述
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884049
Rhziel Fatima Zahrae, Ahsissene Safae, Lahraoua Mohammed, Raissouni Naoufal
{"title":"A Literature Systematic Review of Thermal Infrared Remote Sensing Satellites Land Surface Temperature","authors":"Rhziel Fatima Zahrae, Ahsissene Safae, Lahraoua Mohammed, Raissouni Naoufal","doi":"10.1109/IGARSS46834.2022.9884049","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884049","url":null,"abstract":"Land Surface Temperature (LST) is one of the most important parameters in the physics of land surface processes, combining the outgoing longwave radiation and turbulent heat fluxes at the interface between the atmosphere and the ground surface [1]–[3]. LST data is widely used in a variety of applications. This study conducted a systematic literature review to identify the research status and future trends of the application of Thermal Infrared (TIR) Land Surface Temperature (LST) satellites data during 2011–2021. The statistical analysis results show that the publications of papers related to TIR LST data have been steadily raising during 2011–2021. Keyword analysis illustrated that the most frequently satellites used in LST data applications were Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129644479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
SAR Image Data Augmentation via Residual and Attention-Based Generative Adversarial Network for Ship Detection 基于残差和基于注意力的生成对抗网络的SAR图像数据增强船舶检测
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884798
Yue Guo, Hengchao Li, Wen-Shuai Hu, Wei-Ye Wang
{"title":"SAR Image Data Augmentation via Residual and Attention-Based Generative Adversarial Network for Ship Detection","authors":"Yue Guo, Hengchao Li, Wen-Shuai Hu, Wei-Ye Wang","doi":"10.1109/IGARSS46834.2022.9884798","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884798","url":null,"abstract":"In recent years, generative adversarial networks (GANs) have been successfully applied to generate the SAR images. However, due to the fact that it is more difficult to generate the images than to distinguish the real or fake, GANs usually suffer from the problems of unstable training and mode collapse. As such, a residual and attention-based generative adversarial network (RAGAN) is proposed for SAR data augmentation. Firstly, the directional bounding box is used as a constraint in the RAGAN to limit the position of ship in the generated SAR image, which can be further set as the annotation of the SAR image for ship detection directly. After that, inspired by the residual and attention learning, a residual and attention block (RABlock) and a transposed RABlock (TRABlock) are designed to improve the generator of the RAGAN, thus preventing the whole model from gradient vanishing and suppressing the effects of speckle noise and background to enhance the quality of the generated SAR images. Experimental results on the HRSID data set demonstrate the effectiveness of our RAGAN model in SAR data augmentation for ship detection.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130333105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The Hyperspectral Microwave Photonic Instrument (HYMPI) 高光谱微波光子仪器
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884548
A. Gambacorta, M. Stephen, F. Gambini, J. Santanello, P. Mohammed, D. Sullivan, J. Blaisdell, W. Blumberg, I. Moradi, Yanqiu Zhu, W. McCarty, P. Racette, J. Piepmeier
{"title":"The Hyperspectral Microwave Photonic Instrument (HYMPI)","authors":"A. Gambacorta, M. Stephen, F. Gambini, J. Santanello, P. Mohammed, D. Sullivan, J. Blaisdell, W. Blumberg, I. Moradi, Yanqiu Zhu, W. McCarty, P. Racette, J. Piepmeier","doi":"10.1109/IGARSS46834.2022.9884548","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884548","url":null,"abstract":"We present an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a NASA Instrument Incubation Proposal funded research project aimed at developing a hyperspectral microwave instrument intended for enhanced remote sensing of atmospheric temperature and water vapor from space. This paper provides preliminary results on HyMPI's spectral and noise characteristics and a preliminary demonstration of its enhanced water vapor sensitivity and vertical resolution, with a particular focus on the Earth's Planetary Boundary Layer.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130407667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spectral Super-Resolution for Hyperspectral Image Reconstruction Using Dictionary and Machine Learning 基于字典和机器学习的光谱超分辨率高光谱图像重建
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883055
Swastik Bhattacharya, K. Remane, B. Kindel, Gongguo Tang
{"title":"Spectral Super-Resolution for Hyperspectral Image Reconstruction Using Dictionary and Machine Learning","authors":"Swastik Bhattacharya, K. Remane, B. Kindel, Gongguo Tang","doi":"10.1109/IGARSS46834.2022.9883055","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883055","url":null,"abstract":"Hyperspectral sensors measure the radiance spectrum across hundreds of wavelength channels with a resolution typically on the order of 10 nm represented by the full-width-half-maximum (FWHM). The spectra are used in the study of surface materials in the biological, geological and oceanographic sciences to name a few, utilizing quantitative spectroscopic techniques. The instruments developed to measure such data are expensive due to the increased number of bands, and create large datasets that can be difficult to downlink for a given instance. Repeat cycle of space-borne hyperspectral observations of the earth surface is also less than those of multi-spectral sensors. It becomes incumbent to develop mechanisms that could be cost-effective and give desired results. With this aim, spectral Super-Resolution (SR) is attempted on the Airborne Visible and Infra-Red Imaging Spectrometer (AVIRIS) data to reconstruct the hyperspectral band radiance from equally-spaced narrow multi-spectral bands using dictionary learning, followed by denoising using machine learning. The hyperspectral band radiance are first estimated from 30 selected input multi-spectral bands using dictionary trained through K-Singular Value Decomposition (K-SVD), followed by denoising using Random Forest Regression. An overall Signal-to-Noise Ratio (SNR) of 31.58dB is observed from reconstruction after denoising using Random Forest.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126787381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Self-Supervised Representation Learning Enhances Broad Area Search in Multi-Temporal Satellite Imagery 自监督表示学习增强多时相卫星图像的广域搜索
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884559
Tom Stephens, I. Corley, Adrian Gould, A. Polakiewicz, David McVicar, Carlos Torres, Rose Colangelo, Mario Aguilar-Simon
{"title":"Self-Supervised Representation Learning Enhances Broad Area Search in Multi-Temporal Satellite Imagery","authors":"Tom Stephens, I. Corley, Adrian Gould, A. Polakiewicz, David McVicar, Carlos Torres, Rose Colangelo, Mario Aguilar-Simon","doi":"10.1109/IGARSS46834.2022.9884559","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884559","url":null,"abstract":"We describe the advancement of the classical anomaly detection paradigm to a task-relevant change detection problem. Modern machine learning methods support the development of more sophisticated multi-temporal satellite image analysis. Here, we look at the problem of detecting and distinguishing anthropogenic change from natural change over broad regions around the globe. These tasks are well-suited for machine learning algorithms, however, the creation of large scale annotated satellite imagery datasets with sufficient spatial and temporal resolution is expensive. In this paper, we explore utilizing spatiotemporal self-supervised learning which leverages the natural chronology of the data collection to train generalizable feature extractors for various downstream tasks. This approach is shown to boost downstream performance (+10% F1 score, precision, recall), and reduce training time by 80% for the broad area search problem using multi-temporal Sentinel-2 imagery.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126851285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Novel Calibration Framework for Cubesat Radiometer Constellations 一种新的立方卫星辐射计星座标定框架
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884566
M. Aksoy, John W. Bradburn
{"title":"A Novel Calibration Framework for Cubesat Radiometer Constellations","authors":"M. Aksoy, John W. Bradburn","doi":"10.1109/IGARSS46834.2022.9884566","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884566","url":null,"abstract":"Recent advances in CubeSat technologies have enabled use of radiometers deployed in constellations of these small satellites for Earth and space science missions. Advantages of CubeSats such as their low cost, low mass and volume, and lower power requirements, however, are confronted by the challenges in calibration of their payloads as well as intercalibration of CubeSat constellations due to higher sensitivity to ambient conditions. This paper describes a novel system-level calibration framework, called “ACCURACy” to calibrate CubeSat based radiometer constellations as a single system in their entirety with minimal errors and uncertainties. Artificial constellation simulations have demonstrated that ACCURACy, while maintaining the accuracy levels of ideal calibration scenarios, leads to lower uncertainties in calibrated radiometer products compared to state-of-the-art calibration and intercalibration techniques based on overlapping measurements of the constellation members.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130576232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fine-Grained Continual Learning for SAR Target Recognition SAR目标识别的细粒度连续学习
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884149
Zhicong Zheng, Xiangli Nie, Bo Zhang
{"title":"Fine-Grained Continual Learning for SAR Target Recognition","authors":"Zhicong Zheng, Xiangli Nie, Bo Zhang","doi":"10.1109/IGARSS46834.2022.9884149","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884149","url":null,"abstract":"Synthetic Aperture Radar (SAR) systems work in the open and dynamic environment, and capture new data or new targets continually over time. It requires that SAR target recognition algorithms should have the capability to learn new targets incrementally without forgetting the previously learned targets. Besides, SAR targets of different classes usually have subtle differences which makes the recognition more challenging. In this paper, we propose a fine-grained continual learning algorithm for SAR incremental target recognition. Since data imbalance between old and new classes results in the forgetting of old classes, class-balanced loss is introduced to alleviate this phenomenon. In addition, covariance pooling network is utilized to explore the higher-order statistical information to improve the discrimination of features. Experimental results on real SAR datasets validate the effectiveness of the proposed method.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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