THE LAND COVER CLASSIFICATION USING A FEATURE PYRAMID NETWORKS ARCHITECTURE FROM SATELLITE IMAGERY
Extracting land cover information from satellite imagery is of great importance for the task of automated monitoring in various remote sensing applications. Deep convolutional neural networks make this task more feasible, but they are limited by the small dataset of annotated images. In this paper,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-08-01
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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/XLIII-B3-2020/241/2020/isprs-archives-XLIII-B3-2020-241-2020.pdf |