首页|Researchers from Remote Sensing Technology Institute Report on Findings in Machi ne Learning (Physics-based Machine Learning Emulator of At-sensor Radiances for Solar-induced Fluorescence Retrieval In the O2-a Absorption Band)
Researchers from Remote Sensing Technology Institute Report on Findings in Machi ne Learning (Physics-based Machine Learning Emulator of At-sensor Radiances for Solar-induced Fluorescence Retrieval In the O2-a Absorption Band)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According tonews reporting originating from Ober pfaffenhofen, Germany, by NewsRx correspondents, research stated,“The successfu l operation of airborne and space-based spectrometers in recent years holds the promiseto map solar-induced fluorescence (SIF) accurately across the globe. Mac hine learning (ML) can play animportant role in this effort, but its applicatio n to SIF retrieval methods is in part hindered by the needfor time-consuming ra diative transfer modeling to account for atmospheric effects.”
OberpfaffenhofenGermanyEuropeCybor gsEmerging TechnologiesMachine LearningRemote Sensing Technology Institute
NETL
NSTL
