- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 48th European Microwave Conference (EuMC) - Madrid, Spain (2018.9.23-2018.9.27)] 2018 48th European Microwave Conference (EuMC) - Measurement of Shock Wave and Particle Velocities in Shocked Dielectric Material from Millimeter-Wave Remote Sensing
摘要： A millimeter-wave remote sensing technique is used here as a noninvasive and continuous approach for the real-time measurement of shock wave velocity as well as the velocity of the shocked dielectric material during an impact. Experimental results obtained from planar symmetric impacts on PolyMethyl MethAcrylate (PMMA) cylinders are discussed and demonstrate that the proposed millimeter-wave remote sensing technique is highly convenient for deriving both the velocity of the shock wave and velocity of the shocked PMMA material. The proposed approach is applicable to any dielectric material subject to an impact and is an excellent candidate for deriving the equation of state of shocked materials.
关键词： Millimeter wave remote sensing,equation of state.,velocity measurement,Doppler radar,shock waves
更新于2025-09-04 15:30:14
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Recent Progress and Developments in Imaging Spectroscopy
摘要： The Special Issue (SI) on “Recent Progress and Developments in Imaging Spectroscopy” is a collection of contributions presented at the 10th Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Imaging Spectroscopy (SIG IS), which took place at the University of Zurich, Zurich, Switzerland, between 19 and 21 April 2017. Moreover, the SI was equally open to the global research community actively involved in imaging spectroscopy. EARSeL’s Special Interest Group on Imaging Spectroscopy aims at encouraging interdisciplinary discussions among specialists working with innovative Earth observation methods and technologies. With its 10th workshop in April 2017, the EARSeL SIG IS workshop series celebrated its 20th anniversary, building on the legacy of nine successful previous workshops, whereas the ?rst one took place at the University of Zurich back in 1998.
关键词： Remote Sensing,Imaging Spectroscopy,Earth Observation,EARSeL SIG IS
更新于2025-09-04 15:30:14
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Improved rain-rate and drop-size retrievals from airborne and spaceborne Doppler radar
摘要： Satellite radar remote-sensing of rain is important for quantifying of the global hydrological cycle, atmospheric energy budget, and many microphysical cloud and precipitation processes; however, radar estimates of rain rate are sensitive to assumptions about the rain drop size distribution. The upcoming EarthCARE satellite will feature a 94 GHz Doppler radar alongside lidar and radiometer instruments, presenting opportunities for enhanced global retrievals of the rain drop size distribution. In this paper we demonstrate the capability to retrieve both rain rate and a parameter of the rain drop size distribution from an airborne 94 GHz Doppler radar using CAPTIVATE, the variational retrieval algorithm developed for EarthCARE radar–lidar synergy. For a range of rain regimes observed during the Tropical Composition, Cloud and Climate Coupling (TC4) field campaign in the eastern Pacific in 2007, we explore the contributions of Doppler velocity and path-integrated attenuation (PIA) to the retrievals, and evaluate the retrievals against independent measurements from a second, less attenuated, Doppler radar aboard the same aircraft. Retrieved drop number concentration varied over five orders of magnitude between light rain from melting ice, and warm rain from liquid clouds. Doppler velocity can be used to estimate rain rate over land, and retrievals of rain rate and drop number concentration are possible in profiles of light rain over land; in moderate warm rain, drop number concentration can be retrieved without Doppler velocity. These results suggest that EarthCARE rain retrievals facilitated by Doppler radar will make substantial improvements to the global understanding of the interaction of clouds and precipitation.
关键词： CAPTIVATE,satellite radar remote-sensing,EarthCARE,rain rate,path-integrated attenuation,Doppler radar,retrieval algorithm,drop size distribution
更新于2025-09-04 15:30:14
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Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining
摘要： Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.
关键词： Multi criteria evaluation,India,Geographical information systems,Normalize burn ratio,Data fusion,Forest fire,Remote sensing
更新于2025-09-04 15:30:14
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[IEEE 2018 26th International Conference on Geoinformatics - Kunming, China (2018.6.28-2018.6.30)] 2018 26th International Conference on Geoinformatics - Generative Adversarial Network for Deblurring of Remote Sensing Image
摘要： Deblurring is a classical problem for remote sensing images, which is known to be difficult as an ill-posed problem. A feasible solution for the problem is incorporating various priors into restoration procedure as constrained conditions. However, the learning of priors usually assumes that the blurs in an image are produced by fixed types of reasons, and thus a possible decrease in model’s description ability. In this paper, an end-to-end learned method based on generative adversarial networks (GANs) is proposed to tackle the deblurring problem for remote sensing images. The proposed deblurring model does not need any prior assumptions for the blurs. The proposed method was evaluated on a satellite map image data set and state-of-the-art performance was obtained.
关键词： image deblurring,remote sensing image,loss function,Generative Adversarial Network (GAN)
更新于2025-09-04 15:30:14
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[IEEE 2018 26th International Conference on Geoinformatics - Kunming, China (2018.6.28-2018.6.30)] 2018 26th International Conference on Geoinformatics - An Improved Bag-of-Visual-Word Based Classification Method for High-Resolution Remote Sensing Scene
摘要： Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Yet complex scenes make the task difficult. The Bag-of-Visual-Words (BoVW) method is an effective method for RS scene classification while most BoVW methods only consider local features and ignore the import global features of the scene. This paper aims to improve the traditional scale-invariant feature transform (SIFT) based Bag-of-Visual-Words (BoVW) method which only captures local information by fusing a global feature extracted from deep convolutional neural network (DCNN) for high-resolution remote sensing (HRRS) scene classification. The proposed method enhances representation ability for HRRS scenes by considering local and global features simultaneously and outperforms the sate-of-the-arts for obtaining accuracies of 95% on the widely used UC Merced dataset and SIRI-WHU dataset.
关键词： Bag-of-Visual-Words (BoVW),scene classification,scale-invariant feature transform (SIFT),deep convolutional neural network (DCNN),high-resolution remote sensing (HRRS) scene
更新于2025-09-04 15:30:14
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[IEEE 2018 26th International Conference on Geoinformatics - Kunming, China (2018.6.28-2018.6.30)] 2018 26th International Conference on Geoinformatics - SPARK Processing of Computing-Intensive Classification of Remote Sensing Images: The Case on K-Means Clustering Algorithm
摘要： High performance processing of remote sensing images is an important topic in remote sensing applications. One typical type of remote sensing processing is the iterative computing algorithms such as image classification algorithms, which are often computing-intensive and time-consuming. Recent advancement of cloud computing technologies such as APACHE SPARK has shown great promise for improving the computing performance. This paper presents a MapReduce based approach for parallelizing classification algorithms of remote sensing images on the cloud computing platform. The iterative processing is transformed into iterative Map and Reduce tasks that can be executed in parallel. The K-Means clustering algorithm is experimented with the SPARK cluster deployed on the OpenStack cloud computing platform to illustrate the applicability and effectiveness of the approach.
关键词： cloud computing,classification,distributed computing,remote sensing images
更新于2025-09-04 15:30:14
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[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 - Remote Estimation of Water Storage Variation of Lakes in Tibetan Plateau Over the Past 20 Years
摘要： Changes in water storage of the lakes at the Tibetan Plateau are regarded as one of the most critical indicators of regional hydrological response to climate change. Different to the conventional hydrological approaches, in this study, we investigate the storage change of four lakes basing on a conceptual lake storage model and the most recent (2001-2016) available satellite observation that cover the lakes. The water surface areas and water levels are derived from MODIS and LEGOS altimetry data, respectively. Based on the regression function between water level and lake area, the net water budgets of the four lakes are estimated for the period. The results show that the storage of Ziling Co rose the fastest at the rate of 8.24 billion m3/a in the past decades, and the rising rate of Qinghai lake has also reached 4.5billion m3/a. The Ngoring Lake which located in the same region with Qinghai Lake changed fluctuatingly, but the water storage has risen 4.9 billion m3 from 2001 to 2016. The water storage of Dogaicoring QC increased steadily at the rate of 1.2 billion m3/a.
关键词： Lake water level – area curve,Water storage changes,Remote Sensing
更新于2025-09-04 15:30:14
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[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 Circular Approach to Multi-Class Change Detection in Multitemporal Sentinel-1 SAR Image Time Series
摘要： This paper presents a multitemporal technique for multi-class Change Detection (CD) between pairs of images of a satellite image time series. Changes between different pair of images within a time series must be consistent with each other since images acquired over the same scene are causally related with one another. The temporal consistency of the pixel status can be used to formulate a principle that constrains the CD results within the series to be mutually consistent. This principle co-incides with the conservative property of the change variable and it allows the unsupervised validation of changes detected between arbitrary image pairs. Thus, all images in the series, rather than a single couple, are used in the pair-wise CD. The proposed technique was applied to a dataset of dual-polarized terrain-corrected SAR images acquired by Sentinel-1. Experimental results show the validity of the proposed multitemporal approach in improving the CD results.
关键词： Change Detection,Timeseries analysis,Flood detection,SAR image timeseries,Sentinel-1,Remote sensing
更新于2025-09-04 15:30:14
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[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 - Effects of Pansharpening Methods on Discrimination of Tropical Crop and Forest Using Very High-Resolution Satellite Imagery
摘要： This paper assesses the effect of pansharpening process in classification of tropical crop and forest areas. Supervised classifications based on Support Vector Machine were adopted. Different pansharpening methods using bilinear interpolation technique have been used to merge very high spatial resolution Quickbird multispectral and panchromatic imagery. To develop this study, seven sub-areas were extracted and human segmentations data were created. The quantitative results based on the mean of Probabilistic Rand Index, Variation of Information and Global Consistency Error, computed for all sub-areas, showed similar results by using (0.92, 0.87, 0.87, 1.23, 0,2 respectively) and by not applying (0.93, 0.89, 0.86, 1.23, 0.21 respectively) pansharpening methods.
关键词： Land cover,Pansharpening methods,image processing,Classification,Remote Sensing
更新于2025-09-04 15:30:14