Journal: IEEE Transactions on Geoscience and Remote Sensing

Abbreviation

IEEE Trans. Geosci. Remote Sens

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IEEE

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ISSN

0196-2892
1558-0644

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Publications 1 - 10 of 111
  • Use of a GPS-derived troposphere model to improve InSAR deformation estimates in the San Gabriel Valley, California
    Item type: Journal Article
    Houlié, Nicolas; Funning, Gareth J.; Burgmann, Roland (2016)
    IEEE Transactions on Geoscience and Remote Sensing
  • A New Deep-Learning-Assisted Global Water Vapor Stratification Model for GNSS Meteorology: Validations and Applications
    Item type: Journal Article
    Zhang, Wenyuan; Gou, Junyang; Möller, Gregor; et al. (2024)
    IEEE Transactions on Geoscience and Remote Sensing
    Layer precipitable water (LPW), a water vapor product similar to precipitable water vapor (PWV), reports partial moisture content within a specified vertical range. Compared with PWV data, the latest LPW products can describe more refined distributions and variations in water vapor in the troposphere. Global Navigation Satellite Systems (GNSSs), as a powerful water vapor sensing tool, only provide the opportunity to retrieve all-weather PWV, not LPW products. To this end, we develop the first deep-learning-assisted, global water vapor stratification (GWVS) model to estimate the GNSS LPW within any given vertical range. The proposed model is trained and tested using the global radiosonde data, with the training and testing root mean square error (RMSE) of 0.94 and 1.10 mm for radiosonde LPW, indicating the excellent generalization of the GWVS model. Furthermore, the model is comprehensively validated using the data from the two regional GNSS networks and one global network. The RMSEs of the predicted GNSS LPW from the three GNSS networks compared with the co-located radiosonde LPW are 1.52, 1.80, and 1.54 mm, respectively. To study potential applications, we use the model-derived GNSS LPW products to calibrate Geostationary Operational Environmental Satellite-16 (GOES-16) LPW products and improve the GNSS water vapor tomography technique. Results show that the accuracy of three GOES-16 LPW products is improved by 31.3%, 23.3%, and 17.9%, respectively, and the RMSE of the tomography results is reduced from 2.28 to 1.67g/m(3). Both validation and application results highlight that the GWVS model retrieves the required GNSS LPW products and provides additional value for water-vapor-related studies.
  • A New Detection Algorithm for Coherent Scatterers in SAR Data
    Item type: Journal Article
    Sanjuan-Ferrer, Maria J.; Hajnsek, Irena; Papathanassiou, Konstantinos P.; et al. (2015)
    IEEE Transactions on Geoscience and Remote Sensing
  • Soil Moisture Estimation Using Differential Radar Interferometry: Toward Separating Soil Moisture and Displacements
    Item type: Journal Article
    Zwieback, Simon; Hensley, Scott; Hajnsek, Irena (2017)
    IEEE Transactions on Geoscience and Remote Sensing
  • Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring
    Item type: Journal Article
    Wang, Yuanyuan; Xiang Zhu, Xiao; Zeisl, Bernhard; et al. (2016)
    IEEE Transactions on Geoscience and Remote Sensing
  • Forecasting of Tropospheric Delay Using AI Foundation Models in Support of Microwave Remote Sensing
    Item type: Journal Article
    Ding, Junsheng; Mi, Xiaolong; Chen, Wu; et al. (2024)
    IEEE Transactions on Geoscience and Remote Sensing
    Accurate tropospheric delay forecasts are imperative for microwave-based remote sensing techniques, playing a pivotal role in early warning and forecasting of natural disasters such as tsunamis, heavy rains, and hurricanes. Nevertheless, conventional methods for forecasting tropospheric delays entail substantial computational resources and high network transmission speeds, thereby restricting their real-time applicability in remote sensing operations. In this study, we introduce a novel approach to derive forecasted tropospheric delays using artificial intelligence (AI) weather forecast foundation models (FMs), exemplified by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We assess the accuracy of these forecasts on a global scale employing fifth-generation ECMWF atmospheric re-analysis of the global climate (ERA5) (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5), ground-based Global Navigation Satellite System (GNSS), and in situ radiosonde (RS) measurements as reference data. Our results show that the FM-based scheme outperforms traditional methods in both forecast accuracy and length, with the ability to provide high-accuracy tropospheric delay parameters locally for 15-day forecasts at any location within minutes. Furthermore, the FM scheme still maintains accuracy better than empirical models when forecasting up to ten days in advance. This research demonstrates the potential of AI weather forecast FMs in delivering high-precision tropospheric delay medium-range forecasts and improvements for real-time remote sensing applications.
  • Information mining in remote sensing image archives
    Item type: Journal Article
    Datcu, Mihai; Daschiel, Herbert; Pelizzari, Andrea; et al. (2003)
    IEEE Transactions on Geoscience and Remote Sensing
  • Contextual Transformation Network for Lightweight Remote-Sensing Image Super-Resolution
    Item type: Journal Article
    Wang, Shunzhou; Zhou, Tianfei; Lu, Yao; et al. (2022)
    IEEE Transactions on Geoscience and Remote Sensing
    Current super-resolution networks typically reduce network parameters and multiadds operations by designing lightweight structures, but lightening the convolution layer is often ignored. In this work, we observe that convolutions occupy a high percentage of network parameters in most lightweight super-resolution networks. This motivates us to consider lightening super-resolution networks by replacing convolutions with lightweight convolutions, while maintaining the performance. To achieve this, we propose a lightweight convolution layer named contextual transformation layer (CTL). It can yield efficient contextual features through a context feature extraction module and enrich extracted contextual features through a context feature transformation module. Based on CTLs, we build a lightweight super-resolution network called contextual transformation network (CTN) for remote-sensing image super-resolution. Specifically, we use two CTLs to construct a contextual transformation block (CTB) for hierarchical feature learning. Interleaved with a CTB, a context enhancement module (CEM) is employed to enhance the extracted feature representations. All extracted features are processed by a contextual feature aggregation module for final remote-sensing image super-resolution. Extensive experiments are performed on a remote-sensing image super-resolution benchmark named UC Merced. Our method achieves superior results to the other state-of-the-art methods. To demonstrate the generalization ability of our CTL, we extend our CTN to two relevant tasks: natural image super-resolution and natural image denoising. Experimental results on natural image super-resolution benchmarks (i.e., Set5, Set14, B100, Urban100, and Manga109) and natural image denoising benchmarks (i.e., SIDD and DND) further prove the superiority of our method. Our code is publicly available.
  • On the Consistency of Tropospheric Delays Over Mountainous Terrain Retrieved From Persistent Scatterer Interferometry, GNSS, and Numerical Weather Prediction Models
    Item type: Journal Article
    Shehaj, Endrit; Frey, Othmar; Moeller, Gregor; et al. (2024)
    IEEE Transactions on Geoscience and Remote Sensing
    The tropospheric refraction along the signal path is the same for global navigation satellite systems (GNSS) and radar interferometry. However, different observation geometries, spatiotemporal sampling, signal processing methods, as well as signal wavelengths yield rather complementary measurements. The origin of this research is the question whether tropospheric delays retrieved at GNSS permanent stations can support persistent scatterer interferometry (PSI) processing for the retrieval of surface displacement in mountainous terrain, which is challenging because of spatial gaps due to radar layovers, shadowing, and temporal decorrelation in combination with strong variations of water vapor. We analyze maps of tropospheric path delays obtained by collocation of GNSS-estimated delays and PSI processing of an interferometric stack of Cosmo SkyMed X-band synthetic aperture radar (SAR) data in a mountainous region in Valais, Switzerland. We aim to assess the consistency and differences among the datasets to better understand their ability to sense small-scale structures in the lower atmosphere. In addition, we compare them with maps of tropospheric path delays derived from Consortium for Small-Scale Modelling (COSMO-2) numerical weather model (NWM) data. We investigate several factors affecting the interpolation of the GNSS zenith delays to the locations of the persistent scatterers, such as assumptions in the collocation, network size, and resolution. We assessed the meteorological parameters of the NWM to find potential correlations between specific meteorological conditions and different levels of (dis)agreement of delay maps; a clear correlation was not found. We found that the delays estimated from collocated GNSS measurements and PSI tend to have a different dependency on the terrain altitude. The PSI-derived path delays obtained from the X-band SAR data stack capture small-scale spatial variations also visible in NWM delay maps; whereas, at a larger scale, mismatches are found. It appears that the current GNSS network in the mountainous area of the Valais is not dense enough to capture strongly varying tropospheric refraction. We can conclude that denser networks (with a resolution of 5-10 km) in the interferometric SAR (InSAR) footprint region and a careful choice of the assumptions in our interpolation method would make GNSS more suitable for helping PSI processing.
  • Corrections to “Single-Look SAR Tomography as an Add-On to PSI for Improved Deformation Analysis in Urban Areas” [Oct 16 6119-6137]
    Item type: Other Journal Item
    Siddique, Muhammad Adnan; Wegmüller, Urs; Hajnsek, Irena; et al. (2017)
    IEEE Transactions on Geoscience and Remote Sensing
Publications 1 - 10 of 111