Lu Xu
8 Papers
Lu Xu is an academic researcher. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 1, co-authored 2 publications.
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Papers
Fusing Ultra-Hyperspectral and High Spatial Resolution Information for Land Cover Classification Based on AISAIBIS Sensor and Phase Camera
Fangfang Qu,Shuo Shi,Zhongqiu Sun,Wei Gong,Biwu Chen,Lu Xu,Bowen Chen,Xingtao Tang +7 more
TL;DR: In this article , an optimal fusion and classification strategy based on the complementary advantage information of ultrahyperspectral and high spatial resolution image was proposed for land cover classification of complex scenes.
2
LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery
Zixi Shi,Shuo Shi,Wei Gong,Lu Xu,Binhui Wang,Jia Sun,Bowen Chen,Qiang Xu +7 more
TL;DR: LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery achieved high accuracy using a new inversion strategy that incorporated data fusion and constraint-based EM waveform decomposition method.
1
Potentiality of ultraspectral sensor in biophysical and biochemical vegetation parameter inversion
Shuo Shi,Zixi Shi,Zhongqiu Sun,Wei Gong,Lu Xu,Bowen Chen,Fangfang Qu,Xingtao Tang +7 more
TL;DR: Ultraspectral sensor AisaIBIS has high potential for accurate and efficient inversion of biophysical and biochemical vegetation parameters. The study explores the sensitivity analysis, optimization and inversion accuracy using simulated and experimental data. The results demonstrate the effectiveness of the sensor in extracting LAI, chlorophyll content and brown pigment with high accuracy and reduced inversion time.
1
Precise land cover classification in complex scene based on ultra-hyperspectral data from AisaIBIS sensor
Shuo Shi,Fangfang Qu,Wei Gong,Zhongqiu Sun,Zixi Shi,Lu Xu,Bowen Chen +6 more
TL;DR: A comprehensive framework to explore the optimal precise classification strategy of ultra-hyperspectral data in complex scenes (12 vegetation and non-vegetation classes) and the influence of diverse feature subsets and a range of machine learning classifiers on the precision of ground objects recognition is proposed.
Analysis of Net Primary Productivity Variation and Quantitative Assessment of Driving Forces—A Case Study of the Yangtze River Basin
Chenxi Liu,Shuo Shi,Tong Wang,Wei Gong,Lu Xu,Zixi Shi,Jie Du,Fangfang Qu +7 more
TL;DR: Analysis of Net Primary Productivity Variation and Quantitative Assessment of Driving Forces—A Case Study of the Yangtze River Basin investigates changes in net primary productivity (NPP) and its drivers in the YRB ecosystems. The study finds that NPP has increased over time, with human activities being the dominant driver of this increase.