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Requirements

  • GDAL
  • tqdm
  • Numpy
  • Pillow
  • OpenCV
  • MulticoreTSNE
  • colorcorrect

Usage

Convert RGB-NIR GeoTIFF data into cropped RGB and NIR images

crop_rgb-nir.py separates RGB-NIR GeoTIFF data into RGB and NIR data and crops the each data except for blackout part.
If filename option is specified, it is processed for the specified file with single core cpu.
If not, it is processed for files in the specified input directory with multicore cpu.
If you specified --colorcorrect (-cc) option, cropped images are colorcorrected using colorcorrect module.

python crop_rgb-nir.py -i <path to input dir> -o <path to output dir> --filename <filename> -s <size> -cc

or

python crop_rgb-nir.py -i <path to input dir> -o <path to output dir> -s <size> -cc

Making a list file of training data

make_training_datalist.py makes a list of training data train_files.pkl from cropped RGB images.
make_training_datalist.py saves feature vectors of fc7 layer of pretrained AlexNet as an intermediate result in filename_feature.pkl.
For details, refer to Sec. 3.2 of our paper.

python make_training_datalist.py -i <path to input dir or filename_feature.pkl> -o <path to output dir> -n_d <num of training data> -n_g <square of num of grids>

Synthesis of cloud images

make_clouds.py makes synthesized cloud images using perlin noise.
The size of the cloud can be adjusted by changing NoiseOffset in the PythonCloud/Config.py.

python make_clouds.py -n <the number of cloud images> -o <path to output dir>

Color correction for test data

python color_correct.py --in_dir <path to input dir> --out_dir <path to output dir> --grey_world --stretch

Visualizing a feature space (optional)

feature_space_visualizer.py make an image visualized 2-D feature space from filename_feature.pkl.

python feature_space_visualizer.py -i <path to `filename_feature.pkl`> -o <path to output file> -g_n <square of num of grids>

References

  • PythonCloud/ is refered Python-Cloud repository. We modified the code of this repository for Python3.