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Calibration of the SMAP Radiometer for Ocean Applications 海洋应用SMAP辐射计的校正
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324484
T. Meissner, F. Wentz
{"title":"Calibration of the SMAP Radiometer for Ocean Applications","authors":"T. Meissner, F. Wentz","doi":"10.1109/IGARSS39084.2020.9324484","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324484","url":null,"abstract":"We present and discuss the details of the SMAP radiometer calibration that is used in the NASA/RSS ocean surface salinity algorithm. The values for antenna spillover, noise diode and reflector emissivity are fine-tuned to give accurate antenna and brightness temperature values for the global ocean and the Amazon rain forest. The calibration is dynamically adjusted to account for small calibration drifts over time. An adjustment of the thermal model for the reflector temperature is necessary in order to mitigate biases that occur when the SMAP spacecraft goes in and out of solar eclipse. An important test of the reflector emission is an analysis of SMAP brightness temperature differences between ascending and descending swaths. The radiometric calibration accuracy of the NASA/RSS SMAP salinity data set is better than 0.1 Kelvin, which is necessary for accurate ocean surface salinity retrievals.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126057510","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
On the Use of PRF Dithering for Wide Swath, Fine Resolution InSAR PRF抖动在宽幅、小分辨率InSAR中的应用
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323856
H. Zebker
{"title":"On the Use of PRF Dithering for Wide Swath, Fine Resolution InSAR","authors":"H. Zebker","doi":"10.1109/IGARSS39084.2020.9323856","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323856","url":null,"abstract":"Wide swath radar imaging implies that the time interval to collect each radar pulse echo is large, and can exceed the interpulse period. As it is difficult to both transmit and receive from the same antenna simultaneously, there will be “blind ranges” when the receive and transmit times overlap. Today most wide swath systems address this by segmenting the swath in range, such as in ScanSAR or TOPS mode operation. Some groups have started experimenting with variations of sweepSAR technology, in which the receive antenna tracks the radar echo across the swath. Here we look at minimizing the effect of blind ranges by varying the radar PRF. If the pulse times are selected randomly, the main effect is to raise the noise floor of the echoes. Geocoded magnitude images, interferograms, and correlation images show strong robustness with respect to dithering, demonstrating that choosing essentially random PRFs allows for accurate generation of SAR and InSAR data products.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126058560","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
Self-Supervised Remote Sensing Image Retrieval 自监督遥感图像检索
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323294
Kane Walter, Matthew J. Gibson, A. Sowmya
{"title":"Self-Supervised Remote Sensing Image Retrieval","authors":"Kane Walter, Matthew J. Gibson, A. Sowmya","doi":"10.1109/IGARSS39084.2020.9323294","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323294","url":null,"abstract":"Current remote sensing platforms generate a vast amount of imagery but the best current methods to index and retrieve that data require expensive and difficult to procure labels. In this paper, we aim to address this problem by presenting a performant content based image retrieval (CBIR) system that is capable of indexing and retrieval using only unlabelled data. We investigate the use of self-supervised learning, a method for end-to-end learning of visual features from large datasets. In particular, we investigate the performance of four state-of-the-art self-supervised learning methods: variational autoencoders, bidirectional GANs, colourisation networks and DeepCluster, and evaluate the quality of the representations learned on remote sensing CBIR problems. Experiments on two very high resolution datasets show that the best of these methods, DeepCluster, is able to achieve near parity with supervised transfer learning despite not using any label information.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126170192","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: 3
Preliminary Analysis of Experimental Product for the New Scan Pattern of GPM/DPR GPM/DPR新型扫描模式实验产品初步分析
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323276
S. Seto, T. Kubota, T. Masaki, N. Takahashi, T. Iguchi
{"title":"Preliminary Analysis of Experimental Product for the New Scan Pattern of GPM/DPR","authors":"S. Seto, T. Kubota, T. Masaki, N. Takahashi, T. Iguchi","doi":"10.1109/IGARSS39084.2020.9323276","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323276","url":null,"abstract":"Dual-frequency Precipitation Radar (DPR) on the core satellite of Global Precipitation Measurement (GPM) mission has changed its scan pattern in May 2018. As the high-sensitivity measurement of KaPR has been moved to the outer swath from the inner swath, all pixels are measured by both KuPR and KaPR after the scan pattern change. Experimental product (DPR Version 06X; V6X in short) for the new scan pattern applies dual-frequency algorithm for the outer swath. In this study, precipitation rates and other outputs of V6X are analyzed. Dual-frequency algorithm of V6X shows strong incident angle dependence of precipitation rates in the outer swath.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123256653","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
Comparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric Sar Data 双极化Sar数据预测四极化参数的机器学习方法比较
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324192
K. Blix, M. M. Espeseth, T. Eltoft
{"title":"Comparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric Sar Data","authors":"K. Blix, M. M. Espeseth, T. Eltoft","doi":"10.1109/IGARSS39084.2020.9324192","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324192","url":null,"abstract":"This work evaluates three machine learning methods with respect to their ability of learning the functional relationship between dual-polarimetric (dual-pol) input data and quad-polarimetric (quad-pol) output parameters. We chose to study and compare the learning strength of a Neural Network (NN) approach, two kernel-methods, the Support Vector Machine (SVM) and the Gaussian Process Regression (GPR). Overlapping quad-pol Radarsat-2 (RS2) and dual-pol ScanSAR Sentinel-l (S1) sea ice Synthetic Aperture Radar (SAR) scenes, with 20 minutes time difference, were used for establishing the relationship between the dual-pol input data and corresponding quad-pol output parameters. We then used the learned relationship to predict quad-pol parameters for the overlapping S1 dual-pol scene, and show the results of the three machine learning methods, visually, by showing images of the predicted polarimetric features, and quantitively, by computing statistical performance measures. The results indicate that all three methods have strong learning capacity, however, the computed statistical measures and the visual comparisons suggest the best performance for the GPR model.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123287413","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
The Validation of Snow Cover Product Over High Mountain Asia
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324316
Xu Su, Lingmei Jiang, Gongxue Wang, Jian Wang
{"title":"The Validation of Snow Cover Product Over High Mountain Asia","authors":"Xu Su, Lingmei Jiang, Gongxue Wang, Jian Wang","doi":"10.1109/IGARSS39084.2020.9324316","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324316","url":null,"abstract":"Many algorithms and products for snow cover have been developed. Then a unified set of “ground truth” data is important to validate snow cover products. In this study, Landsat-8/OLI data processed by linear unmixing algorithm was determined as “ground truth” to validate the moderate resolution snow products. In order to evaluate the cloud removing effect of the daily fractional snow cover (FSC) dataset of MODIS over High Asia, we use the MOD10A1 FSC product which is calculated by recommended equations as the before cloud removing data, then the Landsat-8/OLI FSC was used to validate both of the MODIS data. The results show that when the percentage of cloud pixels is less than 10%, the binary accuracy can reach 0.85 or more, the mean absolute error is less than 0.25, and the root mean square error is less than 0.35. These results suggest that the product has high credibility, despite there is still a small amount of cloud in the product.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123297381","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
Scene Edge Target Recovery of Scanning Radar Angular Super-Resolution Based on Data Extrapolation 基于数据外推的扫描雷达角超分辨率场景边缘目标恢复
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323387
Deqing Mao, Yongchao Zhang, Yao Kang, Yin Zhang, Weibo Huo, Yulin Huang, Jianyu Yang
{"title":"Scene Edge Target Recovery of Scanning Radar Angular Super-Resolution Based on Data Extrapolation","authors":"Deqing Mao, Yongchao Zhang, Yao Kang, Yin Zhang, Weibo Huo, Yulin Huang, Jianyu Yang","doi":"10.1109/IGARSS39084.2020.9323387","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323387","url":null,"abstract":"Radar antenna can work in scanning mode to obtain a wide region observation. However, for the targets located at the scene edge, the targets are only swept by less than half of the radar beam. Therefore, the scene edge targets are recovered distortedly using the conventional angular super-resolution methods. To keep the performance of recovered targets in the full scene, in this paper, a data extrapolation-based parallel iterative adaptive approach (PIAA) is proposed. First, we analyze the cause of scene edge target distortion. Then, the echo data is extrapolated by half of the radar beam to compensate the unobserved data. Last, a parallel iterative adaptive approach is proposed to recover the targets efficiently. Simulation data is applied to verify the proposed method.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"30 3 Suppl 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123438149","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
Leveraging Space and Ground Assets in A Sensorweb for Scientific Monitoring: Early Results and Opportunities for the Future 利用传感器网络中的空间和地面资产进行科学监测:早期结果和未来机遇
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324049
Steve Ankuo Chien, J. Boerkoel, J. Mason, Daniel Wang, A. Davies, J. Mueting, V. Vittaldev, V. Shah, I. Zuleta
{"title":"Leveraging Space and Ground Assets in A Sensorweb for Scientific Monitoring: Early Results and Opportunities for the Future","authors":"Steve Ankuo Chien, J. Boerkoel, J. Mason, Daniel Wang, A. Davies, J. Mueting, V. Vittaldev, V. Shah, I. Zuleta","doi":"10.1109/IGARSS39084.2020.9324049","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324049","url":null,"abstract":"Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, weather, and many other phenomena. New challenges exist to rapidly assimilate available data and to optimize measurements (e.g. direct assets) to best observe these complex and dynamic spatiotemporal phenomena. Artificial Intelligence offers the potential to assist in data interpretation and resource allocation to best allocate sensing assets. We describe efforts to build and experiment with such “sensorweb” systems and offer some direction for the future sensorweb observation systems.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"11 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123666615","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: 4
Multipass SAR Processing for Radar Depth Sounder Clutter Suppression, Tomographic Processing, and Displacement Measurements 雷达测深器杂波抑制、层析成像处理和位移测量的多通SAR处理
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324498
Bailey Miller, Gordon Ariho, J. Paden, E. Arnold
{"title":"Multipass SAR Processing for Radar Depth Sounder Clutter Suppression, Tomographic Processing, and Displacement Measurements","authors":"Bailey Miller, Gordon Ariho, J. Paden, E. Arnold","doi":"10.1109/IGARSS39084.2020.9324498","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324498","url":null,"abstract":"Differential Interferometric Synthetic Aperture Radar (DIn-SAR) processing techniques applied to ice penetrating radar enable precise measurement of the vertical displacement of englacial layers within an ice sheet. This technique has primarily been applied using ground based ice-penetrating radar due to the ability to achieve a near-zero spatial baseline. We investigate this technique on data from the Multichannel Coherent Radar Depth Sounder (MCoRDS), an airborne ice penetrating radar, and produce initial results from a high accumulation region near Camp Century in northwest Greenland. We estimate the vertical displacement by compensating for the spatial baseline using precise trajectory information and estimates of the cross-track layer slope from direction of arrival analysis. The measurement accuracy is still being investigated.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123697202","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
DETECTION OF SUB-PIXEL PLASTIC ABUNDANCE ON WATER SURFACES USING AIRBORNE IMAGING SPECTROSCOPY 航空成像光谱法检测水面亚像素级塑料丰度
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323556
A. Hueni, Stefan Bertschi
{"title":"DETECTION OF SUB-PIXEL PLASTIC ABUNDANCE ON WATER SURFACES USING AIRBORNE IMAGING SPECTROSCOPY","authors":"A. Hueni, Stefan Bertschi","doi":"10.1109/IGARSS39084.2020.9323556","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323556","url":null,"abstract":"This work tested the practical mapping of floating plastic waste at surface area abundances between 1% - 5% using airborne imaging spectroscopy. APEX and AVIRIS-ng sensors were flown over deployed targets of known abundance during the ESA HyperSense campaign in 2018. Results show that such low abundances can be detected and mapped, while actual material identifications start to fail for abundances of 2.5% and lower.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123722918","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: 6
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