- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusion of Sentinel-1 and Sentinel-2 Images for Classification of Agricultural Areas Using a Novel Classification Approach
摘要： A continuously growing world population increases steadily the demand of foods. This results in strong changes that occur on agricultural sites. Remote sensing data provides an excellent opportunity to monitor these changes which is a crucial base to assess the impact of these changes on the climate or the natural resources. In the presented study, we tested the performance of a new crop classification method for a stack of Sentinel-1 (S1) and Sentinel-2 (S2) images taken within one growing season. We proved, that the new PSP method performs better for S1 images revealing an overall accuracy (OA) of 75% compared to 60% for the Random Forest classifier (RF). The PSP method outperformed also for the fused dataset of S1 and S2 images (72% OA for PSP, 62% for RF). The results illustrate the benefits for crop classifications provided by PSP and give crucial insights for the advantages and limits of S1 and S2 data fusion.
关键词： Classification,Fusion,Agriculture,Sentinel-1,Sentinel-2
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Discrimination with Deep Convolutional Neural Networks in Sar Images
摘要： With the advantages of all-time, all-weather, and wide coverage, synthetic aperture radar (SAR) systems are widely used for ship detection to ensure marine surveillance. However, the azimuth ambiguity and buildings exhibit similar scattering mechanisms of ships, which cause false alarms in the detection of ships. To address this problem, self-designed deep convolutional neural networks with the capability to automatically learn discriminative features is applied in this paper. Two datasets, including one dataset reconstructed from IEEEDataPort SARSHIPDATA and the other constructed from 10 scenes of Sentinel-1 SAR images, are used to evaluate our approach. Experimental results reveal that our model achieves more than 95% classification accuracy on both datasets, demonstrating the effectiveness of our approach.
关键词： ship discrimination,Sentinel-1 images,synthetic aperture radar,deep convolutional neural networks
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Tool for Unsupervised Flood Mapping Using Sentinel-1 Images
摘要： In this paper, we present a novel method for mapping flooded areas exploiting Sentinel-1 ground range detected products. The work introduces two novelties. As first, the input products. In fact, as far we know, no applications using these products has been so far presented in literature. Secondly, a new unsupervised methodology, based on the usage of opportune layers combined in a fuzzy decision system, is presented. Experimental results, obtained both on the single SAR image and on a couple of acquisitions in a change detection framework showed that our method is able to outperform the most popular classification techniques in terms of standard assessment parameters.
关键词： flooding,sentinel-1,classification,fuzzy systems,Synthetic aperture radar
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Field Observations of Temporal Variations of Surface Soil Moisture: Comparison with Insar Sentinel-1 Data
摘要： In this paper we summarize the results of an experiment aiming to compare soil moisture estimates obtained by Sentinel-1 interferometric data with in-situ measurements. The study area, located close to Lisbon in Companhia das Lezirias, Portugal is characterized by a flat topography, large agricultural areas and sparse vegetation. In a test site, four soil moisture sensors were deployed and soil moisture was measured (at a depth of 5 cm) for a period of 7 months in an hourly basis. For the same interval of time and with a temporal resolution of 6 days C-band Sentinel-1 SAR images were interferometrically processed and coherence, phase and phase triplet images were derived. The in-situ soil moisture measurements have been used to predict the analytical interferometric phases, coherences and phase triplets and compared with the measured interferometric phases in both VV and VH polarimetric channels. As a further analysis, a regression analysis of in-situ soil moisture measurement and Sentinel-1 backscattering images has been carried out.
关键词： soil moisture,C-band,SAR interferometry,Sentinel-1
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Towards Unsupervised Flood Mapping Generation Using Automatic Thresholding and Classification Aproaches
摘要： This paper presents an unsupervised flood monitoring approach developed by DARES TECHNOLOGY using Synthetic Aperture Radar (SAR) space-borne images in order to improve disaster management and coordinate response activities in front of flood crisis events. The methodology developed combines SAR pre-processing, histogram thresholding in blocks with bi-modal behavior and unsupervised segmentation approaches. The use of interferometric parameters, such as the coherence is also employed to refine results. The challenge of this detection approach is working towards an unsupervised approach, i.e., training data is no required and, therefore, information or ground-truth about the class statistics within the area of interest are not necessary. The methodology proposed will be validated over the floods occurred in Mumbai city on August 29, 2017 produced by heavy rain events. 4 Sentinel-1 SAR images will be employed for this purpose.
关键词： Flood Monitoring,Sentinel-1,SAR
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Operational Agricultural Flood Monitoring with Sentinel-1 Synthetic Aperture Radar
摘要： Agricultural flood monitoring is important for food security and economic stability. Synthetic Aperture Radar (SAR) has the advantage over optical data by operating at wavelengths not impeded by cloud cover or a lack of illumination. This characteristic makes SAR a potential alternative to optical sensors for agricultural flood monitoring during disasters. The purpose of this study is to assess the effectiveness of using freely available Copernicus Sentinel-1 SAR data for operational agricultural flood monitoring in the United States (U.S.). The operational detection of flood inundation was tested during Hurricane Harvey in 2017, which resulted in significant flooding over Texas and Louisiana, U.S. This paper presents 1) the agricultural flood monitoring method that utilizes Sentinel-1 SAR, the NASS 2016 Cultivated Layer, and the NASS 2016 and 2017 Cropland Data Layers; 2) flood detection validation results and 3) inundated cropland and pasture acreage estimates. The study shows that Sentinel-1 SAR is an effective and valuable data source for operational disaster assessment of agriculture.
关键词： Hurricane Harvey,Flood Detection,Synthetic Aperture Radar,Agriculture Flood Monitoring,Sentinel-1
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Evaluation of the potentiality of polarimetric C- and L-SAR time-series images for the identification of winter land-use
摘要： Land cover and land use monitoring, particularly during winter season, is still a major environmental challenge. Indeed, the presence of a vegetation cover, the dates of sowing, the length of the intercrop period, and land use types have an impact on pollutant transport to water bodies. The objective of this study was to evaluate the potentiality of polarimetric C- and L-SAR time-series to improve the identification and characterization of vegetation cover during winter season in a 130 km2 area. Alos-2, Radarsat-2 and Sentinel-1 time-series were classified using RF algorithm. The best results were obtained from Radarsat-2 polarimetric images acquired between August 2016 and May 2017, with an overall accuracy of ~ 80%.
关键词： Alos-2,Polarimetric SAR,agricultural monitoring,land-use,Sentinel-1,Radarsat-2
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Incorporating Incidence Angle Variation into Sar Image Segmentation
摘要： We present a new approach for incorporating incidence angle derived synthetic aperture radar (SAR) brightness variation directly into SAR image analysis. This approach is unique in that the incidence angle dependency is modeled explicitly into the probability density function rather than an image-wide pre-processing ‘correction’. It can then be used for supervised and unsupervised image analysis, and is notably able to account for a different dependency rate for each class. This has potential benefits for wide-swath SAR imagery over flat areas and ocean, wide angled airborne and UAV based SAR data, connecting narrow-beam SAR images at different acquisition angles, as well as land-based analysis with local topographic terrain angles. An initial example demonstrates unsupervised image segmentation applied to sea ice mapping for meteorological services and climate science, and is compared to the same algorithm without the incidence angle modeling.
关键词： Terrain Correction,Sentinel-1,Wide-swath imagery,Synthetic Aperture Radar,Incidence Angle Correction
更新于2025-09-23 15:21:21
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Physics-Based Modeling of Active-Passive Microwave Covariations for Geophysical Retrievals
摘要： Combined active-passive remote sensing has the potential for capturing the relative advantage of each sensing approach in geophysical retrievals. One cornerstone of combined active-passive microwave sensing is the modeling of the covariation of active and passive signals, which arise from equivalent sensitivities of both sensor types to changes in geophysical properties. In this research contribution, we propose a physics-based active-passive combination of active and passive microwave observations based on Kirchhoff’s law of energy conservation. This allows establishing a physics-based forward model as well as a fully data-driven, single-pass retrieval methodology for active-passive microwave covariation. The forward model and the retrieval approach are adaptable to different sensor characteristics (incidence angle, frequency & polarization). The theoretical (forward model) as well as applied (retrieval method) physics-based covariation framework is tested with SMAP (LL) and SMAP/Sentinel-1 (LC) data to reveal potentials and constraints for active-passive microwave sensing. As a result of the conducted study, a linear functional relationship between active and passive microwave observations (e.g. assumed for the SMAP mission) is confirmed, if higher-order scattering can be omitted.
关键词： microwave covariation,active-passive microwave sensing,Sentinel-1,SMAP,multi-frequency
更新于2025-09-23 15:21:21
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Exploring Sentinel-L Data for Local Climate Zone Classification
摘要： Local climate zone (LCZ) is a categorical scheme describing the morphology of urban area, which is a valuable not only for the original purpose of temperature study, but also for other urban oriented studies like population density estimation and economical development monitoring. Standard LCZ production works only on individual cities using merely optical data, mostly LandSat-8 data. Our goal is to develop a framework that 1) can potentially work on a large number of cities, i.e., training on number of cities and testing on number of other cities; 2) exploits Synthetic Aperture Radar (SAR) data. In this paper, we investigated the potential of Sentinel-1 Dual-Pol data on producing LCZ maps in general. It shows the Sentinel-1 data could improve the classification accuracy of several LCZ classes. Joint use of LandSat-8 data, OpenStreetMap (OSM) data and Sentinel-1 data provide 62.05% over-all accuracy, which is higher than 51.20% achieved by using only LandSat-8 and OSM data.
关键词： Local Climate Zone (LCZ),LandSat-8,Sentinel-1,Classification,Dual-Pol
更新于2025-09-23 15:21:21