Geoscience and Remote Sensing
Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.
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Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers.
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The theoretical data sets are used in the synthetic tests in the manuscript and supplementary materials calculated by the models.
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Thermal infrared (IR) environmental satellite data assimilation and remote sensing of the surface and lower troposphere depend on accurate specification of the spectral surface emissivity within clear-sky forward calculations. Over ocean surfaces, accurate modeling of surface-leaving radiances over the sensor scanning swaths is complicated by a quasi-specular bidirectional reflectance distribution function (BRDF).
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Detection of impact craters on the surface of Mars is a critical component in the study of Martian geomorphology and the evolution of the planet. As one of the most distinguishable geomorphic units on the Martian surface, accurate determination of the boundaries of impact craters provides valuable information in mapping and research efforts. The topography on Mars is more complex than that of the moon, making detection of real impact crater boundaries a challenging task.
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This dataset consists of two parts:
1)original VSP data
---- syn.txt -> synthetic VSP record
---- SEAM.xlsx -> the 66-th shot of SEAM Phase I RPSEA Elastic Simulations
---- aN.txt -> real VSP record in the Dong area
2)coressponding up- and downgoing separation results
---- results_Syn -> abalation experimental results for self validation
---- results_SEAM -> comparison experimental results on SEAM open data
---- results_Dong -> comparison experimental results on real VSP data
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Synthetic Aperture Radar (SAR) satellite images are used increasingly more for Earth observation. While SAR images are useable in most conditions, they occasionally experience image degradation due to interfering signals from external radars, called Radio Frequency Interference (RFI). RFI affected images are often discarded in further analysis or pre-processed to remove the RFI.
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