Using the pre-trained ImageNet models and cyclical learning rates, we tried to classify the DeepSAT-6 dataset (https://csc.lsu.edu/~saikat/deepsat/) into 6 categories (barren land, trees, grassland, roads, buildings and water bodies). Due to absence of occlusion by cloud we dropped the NIR-channel of the data.
Using the pre-trained ImageNet models and cyclical learning rates, we tried to classify the DeepSAT-6 dataset (https://csc.lsu.edu/~saikat/deepsat/) into 6 categories (barren land, trees, grassland, roads, buildings and water bodies). Due to the absence of occlusion by the cloud, we dropped the NIR channel of the data.
abhijitpal1247/DeepSAT-6-Satellite-Image-Classification
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
Using the pre-trained ImageNet models and cyclical learning rates, we tried to classify the DeepSAT-6 dataset (https://csc.lsu.edu/~saikat/deepsat/) into 6 categories (barren land, trees, grassland, roads, buildings and water bodies). Due to the absence of occlusion by the cloud, we dropped the NIR channel of the data.
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published