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王明常
Mingchang Wang
(教授)
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学位:博士
性别:男
学历:博士研究生毕业
在职信息:在职
所在单位:地球探测科学与技术学院
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Integrated Risk Assessment of Agricultural Drought Disasters in the Major Grain-Producing Areas of Jilin Province, China. Land. 2023,12 (1):160
TDA-Net: A Novel Transfer Deep Attention Network for Rapid Response to Building Damage Discovery. Remote Sensing. 2022,14 (15):1-18
Exploring the Optimal Feature Combination of Tree Species Classification by Fusing Multi-Feature and Multi-Temporal Sentinel-2 Data in Changbai Mountain. Forests. 2022,13 (7)
A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images. sensors. 2022
基于GEE的东北三省城市建设用地扩张研究. 吉林大学学报(地球科学版). 2022
领域知识优化深度置信网络的遥感变化检测. 武汉大学学报 信息科学版. 2022
Research on generalized RQD of rock mass based on 3D slope model established by digital close-range photogrammetry. Remote Sensing. 2022
A Generalized 9-Intersection Model for Topological Relations between Regions with Holes. ISPRS International Journal of Geo-Information. 2022
Literature analysis of Dendrolimus disasters based on remote sensing monitoring. Global Geology. 2022
A Dual-channel Fully Convolutional Network for Land Cover Classification Using Multi-feature Information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022
A Residual Attention and Local Context-Aware Network for Road Extraction from High-Resolution Remote Sensing Imagery. Remote Sensing. 2021
A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces. Remote Sensing. 2021
A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data. Remote Sensing. 2021
Boundary Extraction of Urban Built-Up Area Based on Luminance Value Correction of NTL Image. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021
A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images. REMOTE SENSING. 2020
The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China. SENSORS. 2020,20 (18):1-19
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