The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 18, 2025
Filed:
Aug. 23, 2024
University of Science and Technology of China, Hefei, CN;
Cheng Liu, Hefei, CN;
Chengzhi Xing, Hefei, CN;
Qihua Li, Hefei, CN;
Wei Tan, Hefei, CN;
Haoran Liu, Hefei, CN;
Xiangguang Ji, Hefei, CN;
Qihou Hu, Hefei, CN;
UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA, Hefei, CN;
Abstract
Disclosed in the present invention is a method for whole-time space-air-ground integrated hyperspectral stereoscopic remote sensing, tracing and prediction of greenhouse/pollution gas, comprising: performing remote sensing of multi-source heterogeneous data: utilizing a hyperspectral stereoscopic remote sensing device, a hyperspectral imaging device, a hyperspectral unmanned aerial vehicle remote sensing device, a hyperspectral greenhouse gas remote sensing device based on grating light splitting and a night hyperspectral stereoscopic remote sensing device to perform the remote sensing of the multi-source heterogeneous greenhouse/pollution gas data; performing tracing and early warning of greenhouse/pollution gas components: progressively realizing tracing of greenhouse/pollution gas components at different locations based on the multi-source heterogeneous greenhouse/pollution gas data, and performing emission early warning according to a traced result; and performing stereo fusion and prediction of the multi-source heterogeneous data: performing data feature fusion based on the multi-source heterogeneous greenhouse/pollution gas data, and performing prediction of the greenhouse/pollution gas components at a future moment based on a fusion result. The system can complement the deficiencies of existing greenhouse/pollution gas monitoring and tracing technologies.