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Zhenhong Li
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

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Research letter
Published: 07 July 2021 in Geophysical Research Letters
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Three major subduction earthquakes occurred on March 20, 2012 (Mw 7.4), February 16, 2018 (Mw 7.2), and June 23, 2020 (Mw 7.4) in the southwestern coast of Mexico, which caused fatalities, casualties, considerable damage, and raised safety concerns about future seismic hazards. We use satellite geodetic observations to invert for the slip distributions of the three events and then investigate their interactions. Coulomb Failure Stress (CFS) induced by their slip both on surrounding thrust and normal faults are calculated. The positive CFS changes, along with the spatial-temporal seismicity evolution, approximate earthquake recurrence rate and interseismic coupling, collectively indicate an increased possibility of a near-future rupture around the areas between the 2018 and 2020 events in Oaxaca. Furthermore, there is a lowered chance of shallow coastal or offshore normal earthquakes but an increased chance of inland normal ruptures.

ACS Style

Chen Yu; Zhenhong Li; Chuang Song. Geodetic Constraints on Recent Subduction Earthquakes and Future Seismic Hazards in the Southwestern Coast of Mexico. Geophysical Research Letters 2021, 48, 1 .

AMA Style

Chen Yu, Zhenhong Li, Chuang Song. Geodetic Constraints on Recent Subduction Earthquakes and Future Seismic Hazards in the Southwestern Coast of Mexico. Geophysical Research Letters. 2021; 48 (13):1.

Chicago/Turabian Style

Chen Yu; Zhenhong Li; Chuang Song. 2021. "Geodetic Constraints on Recent Subduction Earthquakes and Future Seismic Hazards in the Southwestern Coast of Mexico." Geophysical Research Letters 48, no. 13: 1.

Journal article
Published: 03 July 2021 in International Journal of Applied Earth Observation and Geoinformation
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Automatic river bridge detection is a typical and valuable application for SAR image analysis. However, the background information of SAR image is complex, and there are many specious targets with similar features, such as road, ponds and ridges, which usually cause false alarms. And current river bridge detection methods fail to handle these interference efficiently. Therefore, this paper applies deep learning to SAR and proposes a new river bridge detection algorithm, which is named as Single Short Detection-Adaptively Effective Feature Fusion (SSD-AEFF). It can effectively reduce the interference of noisy information, and achieve fast and high-precision detection of river bridges in complex SAR imagery. SSD-AEFF is based on SSD, and AEFF module is innovated to enhance the multi-scale feature maps together with effective Squeeze-Excitation (eSE) module to further fuse effective features and decrease the interference of background information. Additionally, Non-Maximum Suppression (NMS) is used to screen out redundant candidate boxes to produce the final detection result. Moreover, Gradient Harmonizing Mechanism (GHM) loss function is introduced to solve the problem of sample imbalance in the training process. Experimental results on TerraSAR data compared with existing baseline models demonstrate the superiority of the proposed SSD-AEFF algorithm.

ACS Style

Lifu Chen; Ting Weng; Jin Xing; Zhenhong Li; Zhihui Yuan; Zhouhao Pan; Siyu Tan; Ru Luo. Employing deep learning for automatic river bridge detection from SAR images based on Adaptively effective feature fusion. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102425 .

AMA Style

Lifu Chen, Ting Weng, Jin Xing, Zhenhong Li, Zhihui Yuan, Zhouhao Pan, Siyu Tan, Ru Luo. Employing deep learning for automatic river bridge detection from SAR images based on Adaptively effective feature fusion. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102425.

Chicago/Turabian Style

Lifu Chen; Ting Weng; Jin Xing; Zhenhong Li; Zhihui Yuan; Zhouhao Pan; Siyu Tan; Ru Luo. 2021. "Employing deep learning for automatic river bridge detection from SAR images based on Adaptively effective feature fusion." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102425.

Journal article
Published: 01 July 2021 in Remote Sensing
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Recently, deep learning has been successfully and widely used in hyperspectral image (HSI) classification. Considering the difficulty of acquiring HSIs, there are usually a small number of pixels used as the training instances. Therefore, it is hard to fully use the advantages of deep learning networks; for example, the very deep layers with a large number of parameters lead to overfitting. This paper proposed a dynamic wide and deep neural network (DWDNN) for HSI classification, which includes multiple efficient wide sliding window and subsampling (EWSWS) networks and can grow dynamically according to the complexity of the problems. The EWSWS network in the DWDNN was designed both in the wide and deep direction with transform kernels as hidden units. These multiple layers of kernels can extract features from the low to high level, and because they are extended in the wide direction, they can learn features more steadily and smoothly. The sliding windows with the stride and subsampling were designed to reduce the dimension of the features for each layer; therefore, the computational load was reduced. Finally, all the weights were only from the fully connected layer, and the iterative least squares method was used to compute them easily. The proposed DWDNN was tested with several HSI data including the Botswana, Pavia University, and Salinas remote sensing datasets with different numbers of instances (from small to big). The experimental results showed that the proposed method had the highest test accuracies compared to both the typical machine learning methods such as support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and the recently proposed deep learning methods including the 2D convolutional neural network (CNN) and the 3D CNN designed for HSI classification.

ACS Style

Jiangbo Xi; Ming Cong; Okan Ersoy; Weibao Zou; Chaoying Zhao; Zhenhong Li; Junkai Gu; Tianjun Wu. Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification. Remote Sensing 2021, 13, 2575 .

AMA Style

Jiangbo Xi, Ming Cong, Okan Ersoy, Weibao Zou, Chaoying Zhao, Zhenhong Li, Junkai Gu, Tianjun Wu. Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification. Remote Sensing. 2021; 13 (13):2575.

Chicago/Turabian Style

Jiangbo Xi; Ming Cong; Okan Ersoy; Weibao Zou; Chaoying Zhao; Zhenhong Li; Junkai Gu; Tianjun Wu. 2021. "Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification." Remote Sensing 13, no. 13: 2575.

Journal article
Published: 29 June 2021 in Remote Sensing
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Interferometric synthetic aperture radar (InSAR) technology can obtain one-dimensional surface displacements in the radar line of sight (LOS). In the field of mining subsidence, large 3D movements often occur at the same time, and hence InSAR derived one-dimensional LOS displacements can hardly reflect the actual surface motion in mining areas. To realize the monitoring of three-dimensional large surface displacements in mining areas, a method for monitoring three-dimensional large surface displacements in mining areas that combines SAR pixel offset tracking (OT) and an improved mining subsidence model is proposed in this article. First, a new functional relationship between surface subsidence and horizontal movement combined with the characteristics of the overburden rock stress and the deformation characteristics of the fractured rock mass in coal mining areas is established. Then, a three-dimensional surface deformation model is established based on the proposed relationship between surface subsidence and horizontal movement and the radar projection equation, and finally, the optimal parameters of the deformation model are inverted iteratively using LOS deformation results obtained by OT method to retrieve the three-dimensional large displacements of the surface. The significant advantage of the method proposed in this article is that it can accurately acquire the 3D large surface displacements using only two SAR amplitude images with the same imaging geometry. To verify the accuracy and reliability of the proposed algorithm, two scenes of high-resolution spotlight TerraSAR-X images are used in this paper to conduct a three-dimensional surface displacement monitoring experiment on a working panel in the Daliuta mining area in Shaanxi Province, China, based on the proposed method. Experimental monitoring results show that the maximum surface subsidence is approximately 4.5 m, and the maximum horizontal movements in the strike and dip directions are approximately 1.4 m and 1.2 m, respectively. Using GPS measurements to verify the monitoring results, the root mean square error (RMSE) of vertical subsidence is 6.8 cm, and the RMSE of horizontal movement is 7.1 cm. Compared with those in the original mining subsidence model, the accuracies of vertical subsidence and horizontal movement in the proposed model are increased by 28.2% and 37.5%, respectively, which proves the reliability and accuracy of the proposed method.

ACS Style

Bingqian Chen; Han Mei; Zhenhong Li; Zhengshuai Wang; Yang Yu; Hao Yu. Retrieving Three-Dimensional Large Surface Displacements in Coal Mining Areas by Combining SAR Pixel Offset Measurements with an Improved Mining Subsidence Model. Remote Sensing 2021, 13, 2541 .

AMA Style

Bingqian Chen, Han Mei, Zhenhong Li, Zhengshuai Wang, Yang Yu, Hao Yu. Retrieving Three-Dimensional Large Surface Displacements in Coal Mining Areas by Combining SAR Pixel Offset Measurements with an Improved Mining Subsidence Model. Remote Sensing. 2021; 13 (13):2541.

Chicago/Turabian Style

Bingqian Chen; Han Mei; Zhenhong Li; Zhengshuai Wang; Yang Yu; Hao Yu. 2021. "Retrieving Three-Dimensional Large Surface Displacements in Coal Mining Areas by Combining SAR Pixel Offset Measurements with an Improved Mining Subsidence Model." Remote Sensing 13, no. 13: 2541.

Journal article
Published: 16 May 2021 in Remote Sensing
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The integration of multi-source, multi-temporal, multi-band optical, and radar remote sensing images to accurately detect, extract, and monitor the long-term dynamic change of coastline is critical for a better understanding of how the coastal environment responds to climate change and human activities. In this study, we present a combination method to produce the spatiotemporal changes of the coastline in the Yellow River Delta (YRD) in 1980–2020 with both optical and Synthetic Aperture Radar (SAR) satellite remote sensing images. According to the measurement results of GPS RTK, this method can obtain a high accuracy of shoreline extraction, with an observation error of 71.4% within one pixel of the image. Then, the influence of annual water discharge and sediment load on the changes of the coastline is investigated. The results show that there are two significant accretion areas in the Qing 8 and Qingshuigou course. The relative high correlation illustrates that the sediment discharge has a great contribution to the change of estuary area. Human activities, climate change, and sea level rise that affect waves and storm surges are also important drivers of coastal morphology to be investigated in the future, in addition to the sediment transport.

ACS Style

Quantao Zhu; Peng Li; Zhenhong Li; Sixun Pu; Xiao Wu; Naishuang Bi; Houjie Wang. Spatiotemporal Changes of Coastline over the Yellow River Delta in the Previous 40 Years with Optical and SAR Remote Sensing. Remote Sensing 2021, 13, 1940 .

AMA Style

Quantao Zhu, Peng Li, Zhenhong Li, Sixun Pu, Xiao Wu, Naishuang Bi, Houjie Wang. Spatiotemporal Changes of Coastline over the Yellow River Delta in the Previous 40 Years with Optical and SAR Remote Sensing. Remote Sensing. 2021; 13 (10):1940.

Chicago/Turabian Style

Quantao Zhu; Peng Li; Zhenhong Li; Sixun Pu; Xiao Wu; Naishuang Bi; Houjie Wang. 2021. "Spatiotemporal Changes of Coastline over the Yellow River Delta in the Previous 40 Years with Optical and SAR Remote Sensing." Remote Sensing 13, no. 10: 1940.

Journal article
Published: 07 May 2021 in Earth and Space Science
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Precipitable Water Vapor (PWV) from numerical weather models, such as the latest generation of European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis (ERA5) and the ECMWF High RESolution (HRES) models, are important to meteorological studies and to error mitigation of geodetic observations such as Interferometric Synthetic Aperture Radar (InSAR). In this paper, we provide global validations of these new weather models with respect to Global Positioning System (GPS, ∼13,000 stations) and Moderate Resolution Imaging Spectrometer (MODIS, ∼1 km resolution) using data from January 2016 to December 2018 of every one hour. The global standard deviations of the Zenith Tropospheric Delay (ZTD) differences (DSTDs) between weather models and GPS are 1.69 cm for ERA5 and 1.54 cm for HRES. The global PWV DSTDs between weather models and MODIS are 0.34 cm for ERA5 and 0.32 cm for HRES. The two weather models generally perform better in western North America, Europe, and Arctic by having low ZTD DSTDs (<1.3 cm) or PWV DSTDs (<0.3 cm). HRES also has a low ZTD DSTD of less than 1.3 cm in Antarctic, Japan, New Zealand and Africa and outperforms ERA5 in most regions of the world, despite the fact that 83% of the HRES PWV values are temporally interpolated (from 6‐hour to 1‐hour). However, under extreme weather conditions, ERA5 performs better owing to its high temporal resolution (1 hour). Our results can be used as a global reference for evaluating uncertainties when utilizing these weather models.

ACS Style

Chen Yu; Zhenhong Li; Geoffrey Blewitt. Global Comparisons of ERA5 and the Operational HRES Tropospheric Delay and Water Vapor Products With GPS and MODIS. Earth and Space Science 2021, 8, 1 .

AMA Style

Chen Yu, Zhenhong Li, Geoffrey Blewitt. Global Comparisons of ERA5 and the Operational HRES Tropospheric Delay and Water Vapor Products With GPS and MODIS. Earth and Space Science. 2021; 8 (5):1.

Chicago/Turabian Style

Chen Yu; Zhenhong Li; Geoffrey Blewitt. 2021. "Global Comparisons of ERA5 and the Operational HRES Tropospheric Delay and Water Vapor Products With GPS and MODIS." Earth and Space Science 8, no. 5: 1.

Journal article
Published: 02 May 2021 in Remote Sensing
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A catastrophic tailings dam failure disaster occurred in Brumadinho, Brazil on 25 January 2019, which resulted in over 270 casualties, 24,000 residents evacuated, and a huge economic loss. Environmental concerns were raised for the potential pollution of water due to tailings waste entering the Paraopeba River. In this paper, a detailed analysis has been carried out to investigate the disaster conditions of the Brumadinho dam failure using satellite images with different spatial resolutions. Our in-depth analysis reveals that the hazard chain caused by this failure contained three stages, namely dam failure, mudflow, and the hyperconcentrated flow in the Paraopeba River. The variation characteristics of turbidity of the Rio Paraopeba River after the disaster have also been investigated using high-resolution remote sensing images, followed by a qualitative analysis of the impacts on the downstream reservoir of the Retiro Baixo Plant that was over 300 km away from the dam failure origin. It is believed that, on the one hand, the lack of dam stability management at the maintenance stage was the main cause of this disaster. On the other hand, the abundant antecedent precipitation caused by extreme weather events should be a critical triggering factor. Furthermore, the spatiotemporal pattern mining of global tailings dam failures revealed that the Brumadinho dam disaster belonged to a Consecutive Hot Spot area, suggesting that the regular drainage inspection, risk assessment, monitoring, and early warning of tailings dam in Consecutive Hot Spot areas still need to be strengthened for disaster mitigation.

ACS Style

Deqiang Cheng; Yifei Cui; Zhenhong Li; Javed Iqbal. Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster. Remote Sensing 2021, 13, 1775 .

AMA Style

Deqiang Cheng, Yifei Cui, Zhenhong Li, Javed Iqbal. Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster. Remote Sensing. 2021; 13 (9):1775.

Chicago/Turabian Style

Deqiang Cheng; Yifei Cui; Zhenhong Li; Javed Iqbal. 2021. "Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster." Remote Sensing 13, no. 9: 1775.

Original paper
Published: 23 April 2021 in Landslides
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Interferometric Synthetic Aperture Radar (InSAR) enables detailed investigation of surface landslide movements, but it cannot provide information about subsurface structures. In this work, InSAR measurements were integrated with seismic noise in situ measurements to analyse both the surface and subsurface characteristics of a complex slow-moving landslide exhibiting multiple failure surfaces. The landslide body involves a town of around 6000 inhabitants, Villa de la Independencia (Bolivia), where extensive damages to buildings have been observed. To investigate the spatial-temporal characteristics of the landslide motion, Sentinel-1 displacement time series from October 2014 to December 2019 were produced. A new geometric inversion method is proposed to determine the best-fit sliding direction and inclination of the landslide. Our results indicate that the landslide is featured by a compound movement where three different blocks slide. This is further evidenced by seismic noise measurements which identified that the different dynamic characteristics of the three sub-blocks were possibly due to the different properties of shallow and deep slip surfaces. Determination of the slip surface depths allows for estimating the overall landslide volume (9.18 · 107 m3). Furthermore, Sentinel-1 time series show that the landslide movements manifest substantial accelerations in early 2018 and 2019, coinciding with increased precipitations in the late rainy season which are identified as the most likely triggers of the observed accelerations. This study showcases the potential of integrating InSAR and seismic noise techniques to understand the landslide mechanism from ground to subsurface.

ACS Style

Chuang Song; Chen Yu; Zhenhong Li; Veronica Pazzi; Matteo Del Soldato; Abel Cruz; Stefano Utili. Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements. Landslides 2021, 18, 2721 -2737.

AMA Style

Chuang Song, Chen Yu, Zhenhong Li, Veronica Pazzi, Matteo Del Soldato, Abel Cruz, Stefano Utili. Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements. Landslides. 2021; 18 (8):2721-2737.

Chicago/Turabian Style

Chuang Song; Chen Yu; Zhenhong Li; Veronica Pazzi; Matteo Del Soldato; Abel Cruz; Stefano Utili. 2021. "Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements." Landslides 18, no. 8: 2721-2737.

Journal article
Published: 16 April 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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TerraSAR-X add-on for Digital Elevation Measurements (Tan-DEM-X) mission is designed to generate three-dimensional imag-es of the Earth as the first bistatic synthetic aperture radar (SAR). However, few quantitative studies of TanDEM-X DEM quality validation have been conducted specifically in China. This article presents an iterative method to generate high-resolution Tan-DEM-X DEMs and assesses the vertical accuracy with high-accuracy GPS observations, 1 arc second global DEMs available (AW3D30 v2.2, ASTER GDEM v3, SRTM v3.1, NASADEM, and X-band SRTM), and TanDEM-X 90m DEM. The results demonstrate remarkable elevation quality and con-sistency in coastal areas with root mean square error (RMSE) of 1.7m and 90% linear error (LE90) of 0.4m, whereas 3-4 times weaker accuracies in steep mountainous areas. A positive bias of 1-2m for an overall LE90 measure exists in the dense vegetation and steep-slope mountainous areas. TanDEM-X DEM-based InSAR deformation uncertainty simulation indicates a low or even negligible topographic error contribution of 2-4mm in mountainous areas and less than 1mm in coastal areas. It indi-cates that the TanDEM-X DEM performs better than other glob-al DEMs overall and show a better elevation consistence with SRTM C-band DEM in the coastal area. As an excellent source of up-to-date information, the TanDEM-X DEM are expected be an advantage for understanding dynamic land use changes and improving identification and delineation of coastal lands, moun-tainous landslides, and earthquakes disasters.

ACS Style

Peng Li; Zhenhong Li; Keren Dai; Yasir Al-Husseinawi; Wanpeng Feng; Houjie Wang. Reconstruction and Evaluation of DEMs From Bistatic Tandem-X SAR in Mountainous and Coastal Areas of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 5152 -5170.

AMA Style

Peng Li, Zhenhong Li, Keren Dai, Yasir Al-Husseinawi, Wanpeng Feng, Houjie Wang. Reconstruction and Evaluation of DEMs From Bistatic Tandem-X SAR in Mountainous and Coastal Areas of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):5152-5170.

Chicago/Turabian Style

Peng Li; Zhenhong Li; Keren Dai; Yasir Al-Husseinawi; Wanpeng Feng; Houjie Wang. 2021. "Reconstruction and Evaluation of DEMs From Bistatic Tandem-X SAR in Mountainous and Coastal Areas of China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 5152-5170.

Journal article
Published: 28 March 2021 in Remote Sensing
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Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. The limited training samples of HSI images make it hard to use deep learning methods with many layers and a large number of convolutional kernels as in large scale imagery tasks, and CNN-based methods usually need long training time. In this paper, we present a wide sliding window and subsampling network (WSWS Net) for HSI classification. It is based on layers of transform kernels with sliding windows and subsampling (WSWS). It can be extended in the wide direction to learn both spatial and spectral features more efficiently. The learned features are subsampled to reduce computational loads and to reduce memorization. Thus, layers of WSWS can learn higher level spatial and spectral features efficiently, and the proposed network can be trained easily by only computing linear weights with least squares. The experimental results show that the WSWS Net achieves excellent performance with different hyperspectral remotes sensing datasets compared with other shallow and deep learning methods. The effects of ratio of training samples, the sizes of image patches, and the visualization of features in WSWS layers are presented.

ACS Style

Jiangbo Xi; Okan Ersoy; Jianwu Fang; Ming Cong; Tianjun Wu; Chaoying Zhao; Zhenhong Li. Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification. Remote Sensing 2021, 13, 1290 .

AMA Style

Jiangbo Xi, Okan Ersoy, Jianwu Fang, Ming Cong, Tianjun Wu, Chaoying Zhao, Zhenhong Li. Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification. Remote Sensing. 2021; 13 (7):1290.

Chicago/Turabian Style

Jiangbo Xi; Okan Ersoy; Jianwu Fang; Ming Cong; Tianjun Wu; Chaoying Zhao; Zhenhong Li. 2021. "Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification." Remote Sensing 13, no. 7: 1290.

Preprint content
Published: 04 March 2021
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Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project “EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn – industrial – critical infrastructure monitorinG usIng Combined technologies” is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide “explainable recommendations” that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.

ACS Style

Christos Kontopoulos; Nikos Grammalidis; Dimitra Kitsiou; Vasiliki Charalampopoulou; Anastasios Tzepkenlis; Anastasia Patera; Zoe Pataki; Zhenhong Li; Peng Li; Li Guangxue; Qiao Lulu; Ding Dong. An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters. 2021, 1 .

AMA Style

Christos Kontopoulos, Nikos Grammalidis, Dimitra Kitsiou, Vasiliki Charalampopoulou, Anastasios Tzepkenlis, Anastasia Patera, Zoe Pataki, Zhenhong Li, Peng Li, Li Guangxue, Qiao Lulu, Ding Dong. An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters. . 2021; ():1.

Chicago/Turabian Style

Christos Kontopoulos; Nikos Grammalidis; Dimitra Kitsiou; Vasiliki Charalampopoulou; Anastasios Tzepkenlis; Anastasia Patera; Zoe Pataki; Zhenhong Li; Peng Li; Li Guangxue; Qiao Lulu; Ding Dong. 2021. "An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters." , no. : 1.

Journal article
Published: 06 February 2021 in Remote Sensing
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Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

ACS Style

Yuanyuan Fu; Guijun Yang; Xiaoyu Song; Zhenhong Li; Xingang Xu; Haikuan Feng; ChunJiang Zhao. Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sensing 2021, 13, 581 .

AMA Style

Yuanyuan Fu, Guijun Yang, Xiaoyu Song, Zhenhong Li, Xingang Xu, Haikuan Feng, ChunJiang Zhao. Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sensing. 2021; 13 (4):581.

Chicago/Turabian Style

Yuanyuan Fu; Guijun Yang; Xiaoyu Song; Zhenhong Li; Xingang Xu; Haikuan Feng; ChunJiang Zhao. 2021. "Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis." Remote Sensing 13, no. 4: 581.

Journal article
Published: 18 November 2020 in Remote Sensing
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Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

ACS Style

Yuanyuan Fu; Guijun Yang; Zhenhai Li; Xiaoyu Song; Zhenhong Li; Xingang Xu; Pei Wang; ChunJiang Zhao. Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression. Remote Sensing 2020, 12, 3778 .

AMA Style

Yuanyuan Fu, Guijun Yang, Zhenhai Li, Xiaoyu Song, Zhenhong Li, Xingang Xu, Pei Wang, ChunJiang Zhao. Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression. Remote Sensing. 2020; 12 (22):3778.

Chicago/Turabian Style

Yuanyuan Fu; Guijun Yang; Zhenhai Li; Xiaoyu Song; Zhenhong Li; Xingang Xu; Pei Wang; ChunJiang Zhao. 2020. "Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression." Remote Sensing 12, no. 22: 3778.

Journal article
Published: 08 November 2020 in Remote Sensing
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Coastal dams along the Yellow River Delta are built to prevent seawater intrusion. However, land subsidence caused by significant oil, gas and brine extraction, as well as sediment compaction, could exacerbate the flooding effects of sea-level rise and storm surge. In order to evaluate the coastal dam vulnerability, we combined unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) with small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) results to generate an accurate coastal dam digital elevation model (DEM) over the next 10, 30 and 80 years. Sea-level simulation was derived from the relative sea-level rise scenarios published by the Intergovernmental Panel on Climate Change (IPCC) and local long-term tide gauge records. Assuming that the current rate of dam vertical deformation and sea-level rise are linear, we then generated different inundation scenarios by the superposition of DEMs and sea-levels at different periods by way of a bathtub model. We found that the overtopping event would likely occur around Year 2050, and the northern part of the dam would lose its protective capability almost entirely by the end of this century. This article provides an alternative cost-effective method for the detection, extraction and monitoring of coastal artificial infrastructure.

ACS Style

Guoyang Wang; Peng Li; Zhenhong Li; Dong Ding; Lulu Qiao; Jishang Xu; Guangxue Li; Houjie Wang. Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario. Remote Sensing 2020, 12, 3658 .

AMA Style

Guoyang Wang, Peng Li, Zhenhong Li, Dong Ding, Lulu Qiao, Jishang Xu, Guangxue Li, Houjie Wang. Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario. Remote Sensing. 2020; 12 (21):3658.

Chicago/Turabian Style

Guoyang Wang; Peng Li; Zhenhong Li; Dong Ding; Lulu Qiao; Jishang Xu; Guangxue Li; Houjie Wang. 2020. "Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario." Remote Sensing 12, no. 21: 3658.

Conference paper
Published: 14 October 2020 in Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
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Real-time centimetre-level precise positioning from Global Navigation Satellite Systems (GNSS) is critical for activities including landslide, glacier and coastal erosion monitoring, flood modelling, precision agriculture, intelligent transport systems, autonomous vehicles and the Internet of Things. This may be achieved via the real-time kinematic (RTK) GNSS approach, which uses a single receiver and a network of continuously operating GNSS reference stations (CORS). However, existing CORS networks have often been established simply by attempting regular spacing or in clusters around cities, with little consideration of weather, climate and topography effects, which influence the GNSS tropospheric delay, a substantial GNSS positional error and which prevents homogeneous RTK accuracy attainment. Here, we develop a framework towards optimizing the design of CORS ground infrastructure, such that tropospheric delay errors reduce to 1.5 mm worth of precipitable water vapour (PWV) globally. We obtain average optimal station spacings of 52 km in local summer and 70 km in local winter, inversely related to the atmospheric PWV variation, with denser networks typically required in the tropics and in mountainous areas. We also consider local CORS network infrastructure case studies, showing how after network modification interpolated PWV errors can be reduced from around 2.7 to 1.4 mm.

ACS Style

Chen Yu; Nigel T. Penna; Zhenhong Li. Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2020, 476, 20200248 .

AMA Style

Chen Yu, Nigel T. Penna, Zhenhong Li. Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2020; 476 (2242):20200248.

Chicago/Turabian Style

Chen Yu; Nigel T. Penna; Zhenhong Li. 2020. "Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2242: 20200248.

Journal article
Published: 05 October 2020 in Sustainability
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The impact of agricultural cooperatives on apple farmers’ technical efficiency (TE) in China was examined. The cooperatives were divided into two groups: a collective marketing group for farmers and an equivalent non-marketing group that did not provide a marketing service, although other functions remained the same. Using the propensity score matching (PSM) procedure and stochastic production frontier (SPF) modelling, cooperatives’ key functions that potentially increase farmers’ TE can be identified. The results indicate that membership of either group is positively related to yield. However, cooperatives that were not engaged in marketing achieved higher TE than non-members. This suggests that policy makers should encourage cooperatives to focus on activities that do not include direct marketing to increase TE in apple production in China.

ACS Style

Ruopin Qu; Yongchang Wu; Jing Chen; Glyn Jones; Wenjing Li; Shan Jin; Qian Chang; Yiying Cao; Guijun Yang; Zhenhong Li; Lynn Frewer. Effects of Agricultural Cooperative Society on Farmers’ Technical Efficiency: Evidence from Stochastic Frontier Analysis. Sustainability 2020, 12, 8194 .

AMA Style

Ruopin Qu, Yongchang Wu, Jing Chen, Glyn Jones, Wenjing Li, Shan Jin, Qian Chang, Yiying Cao, Guijun Yang, Zhenhong Li, Lynn Frewer. Effects of Agricultural Cooperative Society on Farmers’ Technical Efficiency: Evidence from Stochastic Frontier Analysis. Sustainability. 2020; 12 (19):8194.

Chicago/Turabian Style

Ruopin Qu; Yongchang Wu; Jing Chen; Glyn Jones; Wenjing Li; Shan Jin; Qian Chang; Yiying Cao; Guijun Yang; Zhenhong Li; Lynn Frewer. 2020. "Effects of Agricultural Cooperative Society on Farmers’ Technical Efficiency: Evidence from Stochastic Frontier Analysis." Sustainability 12, no. 19: 8194.

Journal article
Published: 01 October 2020 in Remote Sensing
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Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. However, it remains an open challenge due to the high similarity between water and shadow in SAR images. To address this challenge, a new end-to-end framework based on deep learning has been proposed to automatically classify water and shadow areas in SAR images. This end-to-end framework is mainly composed of three parts, namely, Multi-scale Spatial Feature (MSF) extraction, Multi-Level Selective Attention Network (MLSAN) and the Improvement Strategy (IS). Firstly, the dataset is input to MSF for multi-scale low-level feature extraction via three different methods. Then, these low-level features are fed into the MLSAN network, which contains the Encoder and Decoder. The Encoder aims to generate different levels of features using residual network of 101 layers. The Decoder extracts geospatial contextual information and fuses the multi-level features to generate high-level features that are further optimized by the IS. Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. The classification accuracy reaches 0.8382 and 0.9278 for water and shadow, respectively; while the overall Intersection over Union (IoU) is 0.9076. MSF-MLSAN demonstrates the success of integrating SAR domain knowledge and state-of-the-art deep learning techniques.

ACS Style

Lifu Chen; Peng Zhang; Jin Xing; Zhenhong Li; Xuemin Xing; Zhihui Yuan. A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas. Remote Sensing 2020, 12, 3205 .

AMA Style

Lifu Chen, Peng Zhang, Jin Xing, Zhenhong Li, Xuemin Xing, Zhihui Yuan. A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas. Remote Sensing. 2020; 12 (19):3205.

Chicago/Turabian Style

Lifu Chen; Peng Zhang; Jin Xing; Zhenhong Li; Xuemin Xing; Zhihui Yuan. 2020. "A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas." Remote Sensing 12, no. 19: 3205.

Journal article
Published: 28 September 2020 in Remote Sensing of Environment
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The 2016 Mw 7.8 Kaikōura earthquake represents an extremely complex event involving over ten major crustal faults, altering conventional understanding of multi-fault ruptures. Although evidence for coseismic slip on the Hikurangi subduction interface is controversial, we present afterslip on the subduction zone beneath Marlborough using 13 months of Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) observations. The spatially and temporally correlated atmospheric errors in SAR interferograms are problematic, and hence a new InSAR time series approach, combining the Generic Atmospheric Correction Online Service (GACOS) with a spatial-temporal Atmospheric Phase Screen (APS) filter to facilitate the InSAR time series analysis, is developed. For interferograms with over 250 km spatial extent, we achieve a 0.77 cm displacement RMS difference against GPS, improving 61% from the conventional InSAR time series method (TS). Comparisons between the overlapping region of two independent tracks show an RMS difference of 1.1 cm for the TS-GACOS-APS combined method, improving 54% from the TS method and 27% from using TS with an APS filter only. The APS filter reduces the short wavelength residuals substantially, but fails to remove the long wavelength error even after the ramp removal, revealing that the GACOS correction has played a key role in mitigating long wavelength atmospheric effects. The resultant InSAR displacements, together with the GPS displacements, are used to recover the time-dependent afterslip distribution on the Hikurangi subduction interface, which provides insights for reviewing the co-seismic slip sources, the present status of the subduction plate boundary and future seismic hazards.

ACS Style

Chen Yu; Zhenhong Li; Nigel T. Penna. Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections. Remote Sensing of Environment 2020, 251, 112097 .

AMA Style

Chen Yu, Zhenhong Li, Nigel T. Penna. Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections. Remote Sensing of Environment. 2020; 251 ():112097.

Chicago/Turabian Style

Chen Yu; Zhenhong Li; Nigel T. Penna. 2020. "Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections." Remote Sensing of Environment 251, no. : 112097.

Journal article
Published: 23 September 2020 in Remote Sensing
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The Mw 7.6 Chi-Chi earthquake struck central western Taiwan in 1999. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. Revisiting this earthquake is a challenge, as the precision and coverage of the data sets available are quite poor. Furthermore, the topographic and vegetation coverage complexity of the area prevents coherent radar images. In this paper, radar and optical images, and terrestrial geodetic measurements, were utilised to study the fault. The Particle Swarm Optimization and Okada Inversion (PSOKINV) geodetic inversion package was used with the generalized Akaike’s Bayesian Information Criterion (gABIC) to precisely determine the slip distribution and relative weighting of datasets. Differences in results using the data sets jointly or separately (e.g., under-estimation due to InSAR, inconsistencies in SPOT offsets, smoother slip distribution with gABIC weighting) are observable. Most of the energy was released in the northern part of the fault, where the strike veers toward the east, and mainly at depths less than 4 km. The PSOKINV-gABIC approach is viable for the study of complicated cases such as the Chi-Chi earthquake and can significantly benefit the weight determination and physical realism of the fault geometry.

ACS Style

Marine Roger; Zhenhong Li; Peter Clarke; Chuang Song; Jyr-Ching Hu; Wanpeng Feng; Lei Yi. Joint Inversion of Geodetic Observations and Relative Weighting—The 1999 Mw 7.6 Chi-Chi Earthquake Revisited. Remote Sensing 2020, 12, 3125 .

AMA Style

Marine Roger, Zhenhong Li, Peter Clarke, Chuang Song, Jyr-Ching Hu, Wanpeng Feng, Lei Yi. Joint Inversion of Geodetic Observations and Relative Weighting—The 1999 Mw 7.6 Chi-Chi Earthquake Revisited. Remote Sensing. 2020; 12 (19):3125.

Chicago/Turabian Style

Marine Roger; Zhenhong Li; Peter Clarke; Chuang Song; Jyr-Ching Hu; Wanpeng Feng; Lei Yi. 2020. "Joint Inversion of Geodetic Observations and Relative Weighting—The 1999 Mw 7.6 Chi-Chi Earthquake Revisited." Remote Sensing 12, no. 19: 3125.

Journal article
Published: 18 September 2020 in IEEE Access
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The automatic extraction of airport runway areas from high-resolution Synthetic Aperture Radar (SAR) images is of great research significance in the military and civilian fields. However, it is still challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework is proposed to extract airport runway areas (runways, taxiways, packing lots, and aircrafts) in a fast and automatic manner. The framework is based on the Geospatial Contextual Attention Mechanism (GCAM) for geospatial feature learning and classification, which is employed together with the down-sampling and coordinate mapping modules. To evaluate the performance of the proposed framework, three large-scale Gaofen-3 SAR images with 1m resolution are utilized in the experiment. According to the results, Mean Pixels Accuracy (MPA) and Mean Intersection Over Union (MIOU) of the GCAM are 0.9850 and 0.9536, respectively, which outperform RefineNet, DeepLabV3+, and MDDA. The training time of GCAM for the dataset is 2.25h, and the average testing time for the five SAR images is only 18.15s. Therefore, GCAM can offer rapid and automatic airport detection from high-resolution SAR images with high accuracy, which can further be employed to mark the airport to greatly improve the detection accuracy of the aircrafts.

ACS Style

Siyu Tan; Lifu Chen; Zhouhao Pan; Jin Xing; Zhenhong Li; Zhihui Yuan. Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery. IEEE Access 2020, 8, 173627 -173640.

AMA Style

Siyu Tan, Lifu Chen, Zhouhao Pan, Jin Xing, Zhenhong Li, Zhihui Yuan. Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery. IEEE Access. 2020; 8 (99):173627-173640.

Chicago/Turabian Style

Siyu Tan; Lifu Chen; Zhouhao Pan; Jin Xing; Zhenhong Li; Zhihui Yuan. 2020. "Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery." IEEE Access 8, no. 99: 173627-173640.