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Xu Geng

dblp:120/1048 · DBLP profile ↗
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16ranked-venue papers
2as first author
5since 2021 · last 2024
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 10 · 2 since 2021Artificial intelligence and machine learning · 5 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
3 papers
Transfer learning and domain adaptation · 44% Graph learning · 22% Time series and sequential data · 22%
Interdisciplinary, comprehensive, and emerging computing
3 papers
Smart cities and intelligent transportation · 100%

Topics — the 7 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Transfer learning and domain adaptation › cross-domain transfer
cross-city transfer
0.412019
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction · IJCAI 2019
Machine learning › Graph learning
graph neural network
0.412019
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting · AAAI 2019
Machine learning › Time series and sequential data
spatiotemporal forecasting
0.412019
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction · IJCAI 2019
Smart cities and intelligent transportation › ride-hailing
ride-hailing demand prediction
0.412019
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting · AAAI 2019
Machine learning › Deep learning architectures and training › recurrent neural network
gated recurrent network
0.112019
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting · AAAI 2019
Machine learning › Deep learning architectures and training
recurrent neural network
0.112019
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting · AAAI 2019
Smart cities and intelligent transportation
urban computing
0.112019
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction · IJCAI 2019

Methods — techniques the papers use, named apart from their topics

transfer learning · 0.8spatiotemporal multi-graph convolution network · 0.8region matching · 0.8neural network representation learning · 0.8multi-graph convolution · 0.8fine-tuning · 0.8contextual gated recurrent neural network · 0.8
YearPublicationVenuePosition
2024 Augmented weighted bidirectional feature pyramid network for marine object detection
Jinxiong Gao, Xu Geng, Kaixuan Shao
Expert Syst. Appl.2
2024 PE-Transformer: Path enhanced transformer for improving underwater object detection
Jinxiong Gao, Xu Geng, Hao Tang 0004, Uzair Aslam Bhatti
Expert Syst. Appl.3
2024 A dual-branch feature fusion neural network for fish image fine-grained recognition
Xu Geng, Jinxiong Gao
Vis. Comput.1
2023 Status of the Terra and Aqua Modis Collection 7 L1B
abstract
The MODIS instruments on the Terra and Aqua spacecrafts have successfully operated for more than 23 and 21 years, respectively, and have far exceeded their designed lifetimes of 6 years. The visible, near infrared, and short-wave infrared spectral bands, with wavelengths from 0.41 to 2.2 μm, are calibrated using the on-board solar diffuser. The mid-wave infrared and long-wave infrared spectral bands are calibrated using a blackbody. The sustained calibration and characterization efforts undertaken by the MODIS Characterization Support Team (MCST) have resulted in several upgrades to the Level-1B (L1B) algorithms over the mission lifetime. The latest version of the L1B algorithm, designated as Collection 7 (C7), was developed based upon observed performance of the current operational L1B product (C6.1), changing instrument behavior, and feedback from the science community. This paper provides an overview of the C7 algorithm and its improvements over the previous data collection, as well as some of the updates that have been incorporated since the first delivery in early 2021.
Amit Angal, Xiaoxiong Xiong, Kevin A. Twedt, Tiejun Chang, Xu Geng, Emily J. Aldoretta, Carlos L. Pérez Díaz
IGARSS5
2022 Nonlinear Detector Response of Aqua MODIS Land Imaging Bands
abstract
The detectors in the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any nonlinear behavior in the system response can contribute to errors in NASA’s Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the onboard solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response nonlinearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain nonlinearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.
Kevin A. Twedt, Sarah Henderson, Xiaoxiong Xiong, Amit Angal, Xu Geng
IEEE Trans. Geosci. Remote. Sens.5
2020 On-Orbit Calibration of Terra MODIS VIS Bands Using Polarization-Corrected Desert Observations
abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra spacecraft is completing two decades of successful Earth observations, providing the scientific community with numerous products and supporting applications, such as land surface cover, sea surface temperature, aerosol properties, and vegetation. The 20 reflective solar bands (RSBs) cover a wavelength range from 0.41 to 2.1 μm and are calibrated primarily using a solar diffuser (SD), with lunar measurements and Earth-view (EV) response trends from desert sites used for the response versus scan-angle (RVS) characterization. Prelaunch analysis showed that a few short-wavelength RSBs of Terra MODIS are particularly sensitive to the polarization of the incident light, with on-orbit results further indicating that the polarization sensitivity has experienced changes that are wavelength, mirror side (MS), and scan-angle dependent. Although the primary calibrator, the SD, should provide an unpolarized light, supplemental inputs from the EV response trends used in the RVS characterization are impacted by the Earth scene polarization. The EV trends, uncorrected for polarization, pose a significant challenge in the RVS characterization for the short wavelength bands (3, 8, 9, and 10), therefore impacting the long-term trends and uncertainty characterization. Previous studies from the MODIS Characterization Support Team (MCST) and the National Aeronautics and Space Administration (NASA) Ocean Biology Processing Group (OBPG) have independently estimated the polarization correction to be applied on the calibrated products to mitigate the impacts to some extent. In this article, the RVS is characterized using the EV response trends after correcting for the polarization effects. Results indicate a significant improvement in the long-term trending, reduced uncertainties in the forward prediction, and a more accurate per-pixel uncertainty provided by the uncertainty index (TiI) in the MODIS L1B. This enhanced approach will, therefore, be considered for implementation in the future L1B reprocessing efforts. The enhanced approach also vastly simplifies the process of adding long-term trend corrections on top of the MODIS L1B, which should streamline the science data production in the future.
Amit Angal, Xu Geng, Xiaoxiong Xiong, Kevin A. Twedt, Aisheng Wu, Daniel O. Link, Emily J. Aldoretta
IEEE Trans. Geosci. Remote. Sens.2
2019 Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting
abstract
Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regions. Existing approaches mainly focus on modeling the Euclidean correlations among spatially adjacent regions while we observe that non-Euclidean pair-wise correlations among possibly distant regions are also critical for accurate forecasting. In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting. We first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi-graph convolution. To utilize the global contextual information in modeling the temporal correlation, we further propose contextual gated recurrent neural network which augments recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. We evaluate the proposed model on two real-world large scale ride-hailing demand datasets and observe consistent improvement of more than 10% over stateof-the-art baselines.
Xu Geng, Leye Wang, Lingyu Zhang 0001, Qiang Yang 0001, Jieping Ye, Yan Liu 0002
AAAI1
2019 Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
abstract
Spatio-temporal prediction is a key type of tasks in urban computing, e.g., traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is adopted. However, the development levels of different cities are unbalanced, and still many cities suffer from data scarcity. To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city. More specifically, we first learn an inter-city region matching function to match each target city region to a similar source city region. A neural network is designed to effectively extract region-level representation for spatio-temporal prediction. Finally, an optimization algorithm is proposed to transfer learned features from the source city to the target city with the region matching function. Using citywide crowd flow prediction as a demonstration experiment, we verify the effectiveness of RegionTrans. Results show that RegionTrans can outperform the state-of-the-art fine-tuning deep spatio-temporal prediction models by reducing up to 10.7% prediction error.
Leye Wang, Xu Geng, Xiaojuan Ma, Qiang Yang 0001
IJCAI2
2019 Ridesharing car detection by transfer learning
Leye Wang, Xu Geng, Xiaojuan Ma, Daqing Zhang 0001, Qiang Yang 0001
Artif. Intell.2
2019 Aqua MODIS Electronic Crosstalk Survey: Mid-Wave Infrared Bands
abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Aqua polar-orbiting satellite has long been known to be subjected to electronic crosstalk. Its signatures are clearly visible in images of the lunar disk by various bands, which are routinely obtained during scheduled roll maneuvers. Electronic crosstalk can potentially impact Level 1B products, causing image artifacts such as striping and radiometric bias. However, a comprehensive effort in mapping the sending/receiving bands and detectors, deriving the crosstalk coefficients, and assessing the impact of the crosstalk contamination on the Level 1B product for Aqua's mid-wave IR bands (3.75 - 4.52 μm) had been lacking. In this work, we surveyed lunar images by the Aqua MODIS bands that are connected to electronic output 2 of the SWIR/MWIR FPA, identifying all detectors affected by electronic crosstalk contamination and determined which bands and detectors are sending the contaminating signal. By assuming a linear model to describe the contamination, we derived linear crosstalk coefficients for the bands/detectors concerned from lunar images and used these to generate corrected Earth Level 1B images for the mid-wave IR bands and assessed the impact of the electronic crosstalk on the Level 1B imagery.
Graziela R. Keller, Truman Wilson, Xu Geng, Aisheng Wu, Zhipeng Wang 0001, Xiaoxiong Xiong
IEEE Trans. Geosci. Remote. Sens.3
2019 MODIS Reflective Solar Bands On-Orbit Calibration and Performance
abstract
The design of the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument was driven by the scientific community's desire to have near-daily global coverage at moderate resolution (~1 km) with comprehensive spectral coverage from visible to long-wave infrared wavelengths. Since their launches in 1999 and 2002, respectively, the Terra and Aqua MODIS instruments have made continuous global observations and generated numerous data products to help users worldwide with their studies of the Earth's system and its shortand long-term changes. The 20 reflective solar bands (RSBs) with wavelengths from 0.41 to 2.2 μm collect data at three nadir spatial resolutions: 250 m, 500 m, and 1 km. The solar diffuser (SD) coupled with the SD stability monitor (SDSM) provides a reflectance-based calibration on-orbit. In addition, lunar observations and response trends from pseudoinvariant desert sites are used to characterize the response versus scanangle changes on-orbit. This paper provides a brief overview of MODIS RSB calibration algorithms, as implemented in the latest Level 1B version 6.1, operational activities, on-orbit performance, remaining challenges, and potential improvements. Results from the SD and SDSM measurements show a wavelength and mirrorside-dependent degradation in RSB responses, with the largest degradation at the shortest wavelengths, particularly for Terra MODIS. Aqua MODIS has experienced far less degradation of its optics and on-board calibrators compared with Terra MODIS, resulting in an overall better performance. With the exception of Aqua band 6, there have been no new noisy or inoperable detectors in the RSB of either instrument during postlaunch operations. As the instruments age and continue to endure the space environment, the detectors and the optical systems degrade. The challenges associated with incorporating these onorbit changes to ensure a production of high-quality calibrated L1B data products are also discussed in this paper.
Xiaoxiong Xiong, Amit Angal, Kevin A. Twedt, Hongda Chen 0003, Daniel O. Link, Xu Geng, Emily J. Aldoretta, Qiaozhen Mu
IEEE Trans. Geosci. Remote. Sens.6
2018 Improvements in the On-Orbit Response Versus Scan Angle Characterization of the Aqua MODIS Reflective Solar Bands
abstract
The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) has been chosen by the Global Space-based Inter-Calibration System operational community as the reference sensor in cross-sensor calibration. A number of geostationary orbit and low-earth orbit sensors use the 0.64-μm band from Aqua MODIS as a calibration reference. After over 15 years on-orbit, the performance characteristics of the MODIS instrument have changed, with effects evident at short wavelengths. MODIS employs a reflectance-based calibration using the solar diffuser measurements with the monthly lunar observations facilitating a response versus scan angle characterization on-orbit. As the instrument continues to operate beyond its design lifetime of 6 years, the on-board calibrators alone are insufficient to accurately characterize the instrument's response at all scan angles. This results in a long-term reflectance drift, particularly observed in the 0.64and 0.85-μm bands, while observing the temporally invariant desert sites. Long-term reflectance drifts of up to 2% and 3% are observed at the beginning of scan for the 0.64and 0.85-μm bands, respectively. An approach using earth-view response to supplement the on-board calibrator measurements has been shown to overcome these inadequacies and is now implemented for the 0.64-, 0.85-, 0.46-, and 0.55-μm land bands of Aqua MODIS. This paper presents the details related to the algorithm implementation and an independent evaluation using the Dome Concordia site and deep-convective clouds. This approach has been reviewed, tested, and approved by the MODIS science team and has been implemented in the forward production of the MODIS L1B Collection 6 starting July 9, 2016. This enhanced approach has also been adopted in the MODIS L1B Collection 6.1 reprocess for the entire mission to facilitate an improved quality of downstream science products. The results with the enhanced approach reduce the reflectance drifts to within 0.5% for most cases.
Amit Angal, Xiaoxiong Xiong, Aisheng Wu, Xu Geng, Hongda Chen 0003
IEEE Trans. Geosci. Remote. Sens.4
2017 Aqua and Terra MODIS RSB Calibration Comparison Using BRDF Modeled Reflectance
abstract
The inter-comparison of MODIS reflective solar bands onboard Aqua and Terra is very important for assessment of each instrument's calibration. One of the limitations is the lack of simultaneous nadir overpasses. Their measurements over a selected Earth view target have significant differences in solar and view angles, which magnify the effects of atmospheric scattering and Bidirectional Reflectance Distribution Function (BRDF). In this work, an inter-comparison technique is formulated after correction for site's BRDF and atmospheric effects. The reflectance measurements over Libya desert sites 1, 2, and 4 from both the Aqua and Terra MODIS are regressed to a BRDF model with an adjustable coefficient accounting for calibration difference. The ratio between Aqua and Terra reflectance measurements are derived for bands 1 to 9 and the results from different sites show good agreement. For year 2003, the ratios are in the range of 0.985 to 1.010 for band 1 to 9. Band 3 shows the lowest ratio 0.985 and band 1shows the highest ratio 1.010. For the year 2014, the ratio ranges from approximately 0.983 for bands 2 and 1.012 for band 8. The BRDF corrected reflectance for the two instruments are also derived for every year from 2003 to 2014 for stability assessment. Bands 1 and 2 show greater than 1% differences between the two instruments. Aqua bands 1 and 2 show downward trends while Terra bands 1 and 2 show upward trends. Bands 8 and 9 of both Aqua and Terra show large variations of reflectance measurement over time.
Tiejun Chang, Xiaoxiong Xiong, Amit Angal, Aisheng Wu, Xu Geng
IEEE Trans. Geosci. Remote. Sens.5
2017 Assessment of Terra MODIS On-Orbit Polarization Sensitivity Using Pseudoinvariant Desert Sites
abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) is currently flying on NASA's Earth Observing System Terra and Aqua satellites, launched in 1999 and 2002, respectively. MODIS reflective solar bands in the visible wavelength range are known to be sensitive to polarized light based on prelaunch polarization sensitivity tests. After about five years of on-orbit operations, it was discovered that the polarization sensitivity at short wavelengths had shown a noticeable increase. In this paper, we examine the impact of polarization on measured top-of-atmosphere (TOA) reflectances based on MODIS Collection-6 L1B over pseudoinvariant desert sites. The standard polarization correction equation is used in combination with simulated at-sensor radiances using the second simulation of a satellite signal in the Solar Spectrum, Vector Radiative Transfer Code (6SV). We ignore the polarization contribution from the surface and a ratio approach is used for both 6SV-derived input parameters and observed TOA reflectances. Results indicate that significant gain corrections up to 25% are required near the end of scan for the 412 and 443 nm bands. The polarization correction reduces the seasonal fluctuations in reflectance trends and mirror side ratios from 30% and 12% to 10% and 5%, respectively, for the two bands. Comparison of the effectiveness of the polarization correction with the results from the NASA Ocean Biology Processing Group shows a good agreement in the corrected reflectance trending results and their seasonal fluctuations.
Aisheng Wu, Xu Geng, Andrew Wald, Amit Angal, Xiaoxiong Xiong
IEEE Trans. Geosci. Remote. Sens.2
2014 MODIS instrument operation and calibration improvements
abstract
Terra and Aqua MODIS have successfully operated for over 14 and 12 years since their respective launches in 1999 and 2002. The MODIS on-orbit calibration is performed using a set of on-board calibrators, which include a solar diffuser for calibrating the reflective solar bands (RSB) and a blackbody for the thermal emissive bands (TEB). On-orbit changes in the sensor responses as well as key performance parameters are monitored using the measurements of these on-board calibrators. This paper provides an overview of MODIS on-orbit operation and calibration activities, and instrument long-term performance. It presents a brief summary of the calibration enhancements made in the latest MODIS data collection 6 (C6). Future improvements in the MODIS calibration and their potential applications to the S-NPP VIIRS are also discussed.
Xiaoxiong Xiong, Amit Angal, Sriharsha Madhavan, Daniel O. Link, Xu Geng, Brian Wenny, Aisheng Wu, Hongda Chen 0003, Vince Salomonson
IGARSS5
2014 Time-Dependent Response Versus Scan Angle for MODIS Reflective Solar Bands
abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments currently operate onboard the National Aeronautics and Space Administration (NASA's) Terra and Aqua spacecraft, launched on December 18, 1999 and May 4, 2002, respectively. MODIS has 36 spectral bands, among which 20 are reflective solar bands (RSBs) covering a spectral range from 0.412 to 2.13 μm. The RSBs are calibrated on orbit using a solar diffuser (SD) and an SD stability monitor and with additional measurements from lunar observations via a space view (SV) port. Selected pseudo-invariant desert sites are also used to track the RSB on-orbit gain change, particularly for short-wavelength bands. MODIS views the Earth surface, SV, and the onboard calibrators using a two-sided scan mirror. The response versus scan angle (RVS) of the scan mirror was characterized prior to launch, and its changes are tracked using observations made at different angles of incidence from onboard SD, lunar, and Earth view (EV) measurements. These observations show that the optical properties of the scan mirror have experienced large wavelength-dependent degradation in both the visible and near infrared spectral regions. Algorithms have been developed to track the on-orbit RVS change using the calibrators and the selected desert sites. These algorithms have been applied to both Terra and Aqua MODIS Level 1B (L1B) to improve the EV data accuracy since L1B Collection 4, refined in Collection 5, and further improved in the latest Collection 6 (C6). In C6, two approaches have been used to derive the time-dependent RVS for MODIS RSB. The first approach relies on data collected from sensor onboard calibrators and mirror side ratios from EV observations. The second approach uses onboard calibrators and EV response trending from selected desert sites. This approach is mainly used for the bands with much larger changes in their time-dependent RVS, such as the Terra MODIS bands 1-4, 8, and 9 and the Aqua MODIS bands 8 and 9. In this paper, the algorithms of these approaches are described, their performance is demonstrated, and their impact on L1B products is discussed. In general, the shorter wavelength bands have experienced a larger on-orbit RVS change, which, in general, are mirror side and detector dependent. The on-orbit RVS change due to the degradation of band 8 can be as large as 35% for Terra MODIS and 20% for Aqua MODIS. Vital to maintaining the accuracy of the MODIS L1B products is an accurate characterization of the on-orbit RVS change. The derived time-independent RVS, implemented in C6, makes an important improvement to the quality of the MODIS L1B products.
Junqiang Sun, Xiaoxiong Xiong, Amit Angal, Hongda Chen 0003, Aisheng Wu, Xu Geng
IEEE Trans. Geosci. Remote. Sens.6