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30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine.

, , , , , , , , and . Remote Sensing, 11 (5): 489 (2019)

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Local Deep Descriptor for Remote Sensing Image Feature Matching., , , , , , and . Remote Sensing, 11 (4): 430 (2019)Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data., , , , , , , , and . ISPRS Int. J. Geo-Information, 7 (7): 243 (2018)A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China., , , , , and . Remote Sensing, 6 (4): 3387-3408 (2014)Solution to the rational function model based on the Levenberg-Marquardt algorithm., , and . FSKD, page 2795-2799. IEEE, (2012)30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine., , , , , , , , and . CoRR, (2018)Spectral-spatial multi-feature classification of remote sensing big data based on a random forest classifier for land cover mapping., , , , and . Cluster Computing, 20 (3): 2311-2321 (2017)Information Services of Big Remote Sensing Data., , , , , , , , , and 2 other author(s). BigSDM, volume 11473 of Lecture Notes in Computer Science, page 16-31. Springer, (2018)Assessing Light Pollution in China Based on Nighttime Light Imagery., , , , , and . Remote Sensing, 9 (2): 135 (2017)A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery., , , and . Remote Sensing, 8 (1): 56 (2016)Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images., , , , , , , and . Remote Sensing, 10 (5): 755 (2018)