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6 results for “DInSAR”
Deep learning based automatic grounding line delineation in DInSAR interferograms
<p>This dataset contains a small subset of the AIS_cci GLL product, which covers several key glaciers and the corresponding HED-delineated grounding lines generated from our automatic delineation pipeline. A description of the attributes of the AIS_cci GLL product is provided in the <a href="https://climate.esa.int/media/documents/ST-UL-ESA-AISCCI-PUG-0001.pdf" target="_blank" rel="noopener">Product User Guide</a>. We do not indicate the split of the interferograms into training, validation and test sets as the complete AIS_cci dataset is not open-access.</p> <p>We also provide eight double difference interferograms at 100 m pixel size to demonstrate the generation of the features stack. Please note, eight samples are not sufficient to train the neural network to achieve the delineation capability described in our work.</p> <p>The "UUID" attribute in both GeoJSON files is an identifier that links the vector geometries to the interferogram TiFF files. </p>
Integrated DInSAR + GNSS example data sets
<p>This data repository contains sample datasets of raw DInSAR time series (NSBAS_PARAMS.h5), raw, interpolated GNSS time series maps (GPS_East/North/Up.h5) , errors associated with the GNSS data (GPS_East/North/Up_sigma.h5), and integrated DInSAR + GNSS time series (fused.h5). Details about the data can be read about in the following publication: [Corsa, B. "Integration of DInSAR Time Series and GNSS data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications" <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>(3), 784; <a href="https://doi.org/10.3390/rs14030784">https://doi.org/10.3390/rs14030784</a>]. The raw DInSAR time series spans 245 dates between 2015-11-11 to 2021-04-13 over the Big Island of Hawaii. The current raw GPS data and fused time series used 22 data points between those same dates. </p>
CanEx-SM10 UAVSAR DInSAR soil moisture estimation
<p>Inverted soil moisture m<sub>v</sub> based on the linearized model for the interferometric phase at HH and VV polarization obtained using UAVSAR L-band data acquired during the CanEx-SM10 campaign. The linearized model with the uniform parameterization is applied over the entire image, including to fields that decorrelate during the campaign (e.g. tilling) or to open water bodies. Note that the phase is not atmospherically corrected.</p> <p>linear_hh.mp4: HH polarization</p> <p>linear_vv.mp4: VV polarization</p>
Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
<p>This repository contains the file 'Natalya_Maslennikova_data_2024.zip,' which includes 171 DInSAR interferograms (both phase and coherence of a DInSAR signal) and three versions of the grounding lines: manually mapped ('gl_manual_mapping.shp'), mapped by the neural network ('gl_neural_network.shp'), and mapped using the phase gradient-based approach ('gl_phase_gradient.shp'). The X-band DInSAR data were acquired by the ASI’s COSMO-SkyMed mission between 2020 and 2022 over five East Antarctica glaciers: Stancomb-Wills (STA: 32 interferograms), Veststraumen (VES: 35 interferograms), Jutulstraumen (JUT: 42 interferograms), Moscow University (MOS: 27 interferograms), and Rennick (REN: 35 interferograms).</p> <p>The 'gl_neural_network.shp' and 'gl_phase_gradient.shp' shapefiles contain the following fields: the name of the glacier (the 'Glacier' column), four dates in YYYYMMDD format when the corresponding DInSAR interferogram was acquired (the 'Primary1', 'Secondary1', 'Primary2', 'Secondary2' columns), and the double difference name in 'Primary1_Secondary1-Primary2_Secondary2' format (the 'DD' column). The 'gl_manual_mapping.shp' shapefile contains 'Glacier', 'Primary1', 'Secondary1', 'Primary2', 'Secondary2', and 'DD' columns as well, along with the 'Revisit' column (difference in days between the 'Primary1' and 'Primary2' images were acquired), the 'Time' column (acquisition time in 'HHMMSS' format), the 'Coherence' column (average coherence of the corresponding DInSAR interferogram), and the 'Train_1', 'Train_2', 'Train_3' columns, which contain indicators 'Y' for 'yes' and 'N' for 'no', showing whether or not the DInSAR interferogram was used in the corresponding training session.</p>
Remote Sensing DInSAR Method for Land Subsidence Detection in Semarang, Central Java, Indonesia
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
DInSAR-SBAS mean velocity data of Somma-Vesuvius Volcano from 1992 to 2010
<p>Ascending and descending components of the mean deformation velocities observed at Somma-Vesuvius Volcano from 1992 to 2010, obtained by DInSAR-SBAS processing.</p>
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