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Self-Attention for Raw Optical Satellite Time Series Classification

<!-- ```bibtex @article{russwurm2019self, title={Self-Attention for Raw Optical Satellite Time Series Classification}, author={Ru{\ss}wurm, Marc and K{\"o}rner, Marco}, journal={arXiv preprint arXiv:1910.10536}, year={2019} } ``` -->

Feature extraction through self-attention on Raw Sentinel 2 Time Series First-Layer Attention Heads

Source Code of Rußwurm & Körner (2019), Self-Attention for Raw Optical Satellite Time Series Classification

@article{russwurm2020,
title = "Self-attention for raw optical Satellite Time Series Classification",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
volume = "169",
pages = "421 - 435",
year = "2020",
issn = "0924-2716",
doi = "https://doi.org/10.1016/j.isprsjprs.2020.06.006",
url = "http://www.sciencedirect.com/science/article/pii/S0924271620301647",
author = "Marc Rußwurm and Marco Körner",
}

Architectures

Models

Four deep learning models for time series classification are implemented

Getting started

Python Dependencies

Anaconda environment

conda create -n crop-type-mapping python=3.7.3 pip
conda activate crop-type-mapping
pip install -r requirements.txt
<!-- Build docker container ``` docker build -t croptypemapping . ``` -->

Download Dataset, Tuning Results and Models

download raw Sentinel 2 crop type label dataset to data/BavarianCrops via

bash download.sh dataset

and the pretrained models for hyperparameter tuning and evaluation via

bash download.sh models

or both with bash download.sh all

Experiments are composed of a set of parameters that define model and datasets. Experiments are defined in the if-else cases in src/experiments.py and generally follow the naming convention isprs_<model>_<dataset>_<meta>.

Training

start visdom server by running visdom in conda environment and open http://localhost:8097/ in the browser.

train from scratch (23 classes) with hyperparameters defined in ../models/tune/23classes/transformer_tum.csv. Working directory is src

python train.py --experiment isprs_tum_transformer \
    --store /tmp/ \
    --classmapping ../data/BavarianCrops/classmapping23.csv \
    --hyperparameterfolder ../models/tune/23classes

to continue training with 23 classes point --store to an existing model

python train.py --experiment isprs_tum_transformer \
    --store ../models/train/23classes/0 \
    --classmapping ../data/BavarianCrops/classmapping23.csv \
    --hyperparameterfolder ../models/tune/23classes

experiments on raw dataset: isprs_tum_transformer, isprs_tum_msresnet, isprs_tum_tempcnn, isprs_tum_rnn

classmappings: mapping tables to select 12 or 23 classes to classify

hyperparameter folder: folder with results of ray-tune results implemented in tune.py. hyperparameters summarized in csv files for model and dataset, as defined in src/hyperparameters.py

Hyperparameter Tuning

Ray-Tune allows hyperparameter tuning of multiple models in parallel. Execute in src

python tune.py --experiment transformer_tum \
    -g 0.5 -c 2 -b 64 --local_dir ../models/tune --seed 0 \
    --classmapping $PWD/../data/BavarianCrops/classmapping23.csv \
    --dataroot $PWD/../data

to train two models per GPU and store results in ../models/tune. Valid experiments are [transformer|rnn|msresnet|tempcnn]_[tum|gaf] which are defined in src/hyperparameter.py

External Code

https://github.com/jadore801120/attention-is-all-you-need-pytorch

https://github.com/geekfeiw/Multi-Scale-1D-ResNet

https://github.com/charlotte-pel/igarss2019-dl4sits