CLASSIFICATION OF LAND-COVER THROUGH MACHINE LEARNING ALGORITHMS FOR FUSION OF SENTINEL-2A AND PLANETSCOPE IMAGERY
To monitor and manage the changes in the land use and land cover, is vital the process of classification; machine learning offers the potential for effective and efficient classification of remotely sensed imagery. However, not many articles have explicitly dealt with the effects of image fusion on...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/361/2020/isprs-archives-XLII-3-W12-2020-361-2020.pdf |