A5100762849- Advanced Computational Techniques and Applications
- Data Management and Algorithms
- Flood Risk Assessment and Management
- Remote Sensing and Land Use
- Soil Moisture and Remote Sensing
- Geographic Information Systems Studies
- Remote-Sensing Image Classification
- Hydrology and Watershed Management Studies
- Remote Sensing in Agriculture
- Distributed and Parallel Computing Systems
- Simulation and Modeling Applications
- Climate change and permafrost
- Soil and Unsaturated Flow
- Remote Sensing and LiDAR Applications
- Cryospheric studies and observations
- Geological Modeling and Analysis
- Service-Oriented Architecture and Web Services
- Hydrology and Drought Analysis
- Environmental Changes in China
- Advanced Image and Video Retrieval Techniques
- Advanced Database Systems and Queries
- Plant Water Relations and Carbon Dynamics
- Advanced Image Fusion Techniques
- Advanced Clustering Algorithms Research
- Data Mining Algorithms and Applications
Huazhong University of Science and Technology
2023-2025
Wuhan University
2016-2025
Xi'an Jiaotong University
2023
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
2002-2009
Southwest Jiaotong University
2009
China University of Petroleum, Beijing
2009
Experimental Center of Forestry in North China
2007
Central China Normal University
2007
Large-scale and dynamic surface water mapping is crucial for understanding the impact of global climate change human activities on distribution resources. Remote sensing imagery has become primary data source due to its high spatiotemporal resolution wide coverage. However, reliability current products during flood seasons limited influence clouds optical remote images. Moreover, annual seasonal cannot capture intra-month variations bodies. To address these challenges, we proposed a...
In the task of using deep learning semantic segmentation model to extract water from high-resolution remote sensing images, multiscale feature and extraction have become critical factors that affect accuracy image classification tasks. A single-scale training mode will cause one-sided results, which can lead "reverse" errors imprecise detail expression. Therefore, fusing features for pixel-level is key achieving accurate segmentation. Based on this concept, paper proposes a scheme achieve...
MapReduce has been widely used in Hadoop for parallel processing larger-scale data the last decade. However, remote-sensing (RS) algorithms based on programming model are trapped dense disk I/O operations and unconstrained network communication, thus inappropriate timely analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to datasets Spark, shortages...
A method is proposed for the production of downscaled soil moisture active passive (SMAP) (SM) data by combining optical/infrared with synthetic aperture radar (SAR) based on random forest (RF) model. The leverages sensitivity microwaves to surface SM and triangle/trapezium feature space among vegetation indexes (VIs), land temperature (LST), SM. First, five RF architectures (RF1–RF5) were trained tested at 9 km. Second, a comparison was performed RF1–RF5, evaluated against in situ...
Urban land use/land cover (LULC) classification has long been a hotspot for remote sensing applications. With high spatio-temporal resolution and multispectral, the recently launched GF-6 satellite provides ideal open imagery LULC mapping. In this study, we utilized multitemporal images to generate six types of features, including spectral bands, texture built-up, waterbody, vegetation, red-edge indices. The minimum Redundancy Maximum Relevance (mRMR) algorithm was employed optimize feature...
In this paper, we propose a significance test-based change detection method that can automatically discriminate between changed and unchanged pixels in the difference image. The adaptively considers local contextual information, which is contained neighborhoods of each pixel, to derive decision threshold. our method, test algorithm based on maximuming posteriori estimate constructed; then, weight pixel block imposed increase accuracy. proposed distribution image satisfying Laplace model also...
In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages accurately capturing the satellite footprint scale specific local regional moisture. However, performance most is largely determined by size training and limited generalization ability to accomplish correlation extraction models, which are unsuitable for larger practices. this paper, a deep learning...
China is frequently subjected to local and regional drought disasters, thus, monitoring vital. Drought assessments based on available surface soil moisture (SM) can account for water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, evaluation Soil Moisture Active Passive (SMAP) products inadequate, L-band microwave data have not been applied agricultural throughout China. In this study, first, we provide...
NDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data its changes play an important role forecasting. For this study, we proposed convolutional networks (CNN) combined feature engineering forecasting model (SF-CNN), which integrated both advantages...
Soil moisture (SM) is an indispensable variable in drought monitoring and weather forecast. L-band found to be the most suitable band for retrieving surface SM. Here, we evaluate two passive microwave SM products Moisture Active Passive (SMAP) Ocean Salinity (SMOS) Inner Mongolia. The collected in-situ data from 24 measured sites triple collocation (TC) analysis method (using Global Land Data Assimilation System (GLDAS) Noah Advanced Scatterometer (ASCAT) as a reference dataset) are used...
We present an efficient synthetic pathway for kasugamycin, aminoglycoside antibiotic, utilizing naturally derived carbohydrates as starting materials. This synthesis effectively addresses stereochemical complexities by employing the selective reduction of d-fucal, which generates a crucial 3-deoxyglycal intermediate. intermediate facilitates introduction amino groups at C-2 and C-4 positions, is essential kasugamine. Subsequent glycosylation with glycosyl 1-O-m-chlorobenzoate (mCBz) donors...
Lakes and reservoirs (LaR) are important parts of water resources their rapid accurate monitoring is an essential guarantee for maintaining ecological health social development. The existing waterbody extraction methods mostly targeted at local bodies, with little attention on the national scale. In this letter, improved U-Net method proposed LaR from GF-1 satellite imagery. First, 21 scenes images evenly selected across China, training set validation produced by image processing, cropping,...
Han River mainstream, China. Human activities and climate change are synergistically impacting on the spatiotemporal features of surface water, which affects ecological environment evolution economic development basin. However, long-term changes area spatial characteristics water have not been well quantified for lack sufficient data processing powers. The study produced annual products mainstream from 1986 to 2020 based Google Earth Engine Landsat images, then analyzed dynamics influence...