/multi-temporal-crop-classification

deep learning based multi-temporal crop classification based on Liheng Zhong's paper

Primary LanguagePythonMIT LicenseMIT

Multi-Temporal-Crop-Classification using Deep Learning (work in progress)

  • Recreating code for the paper "Deep Learning Based Multi-Temporal Crop Classification"

  • There are a few minor differences - Zhong et al. use 46 element vector as time series, I use MODIS 16 day composite NDVI data (23 entries per year).

  • cnn-network.py contains code for 1d cnn network described in the paper

  • lstm-network.py contains code for lstm described in the paper

  • evaluate_baselines.py contains code for XGBoost, SVM and RandomForest classification

  • data_generator.py assumes that each time series is stored as a separate csv with the NDVI values stored under the column 'NDVI'. It does nearest neighbor interpolation to fix missing values.

  • Python version and required packages: Python3, Keras, Tensorflow, Scikit

  • Use Hufkens et al. to download MODIS data.

References:

  • [1] Hufkens K. (2017) A Google Earth Engine time series subset script & library. DOI: 10.5281/zenodo.833789
  • [2] Zhong, Liheng, Lina Hu, and Hang Zhou. "Deep learning based multi-temporal crop classification." Remote sensing of environment 221 (2019): 430-443.

Acknowledgements:

  • Bharathkumar Ramachandra (tnybny)