Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP

An interpretable deep learning framework for land use and land cover classification (LULC) in remote sensing using SHAP is introduced. It utilizes a compact CNN model for the classification of satellite images and then feeds the results to a SHAP deep explainer so as to strengthen the classification results. The proposed framework is applied to Sentinel-2 satellite images containing 27000 images of pixel size 64 × 64 and operates on three-band combinations, reducing the model’s input data by 77% considering that 13 channels are available, while at the same time investigating on how different spectrum bands affect predictions on the dataset’s classes. Experimental results on the EuroSAT dataset demonstrate the…

• HM
  H2020 Marie Skłodowska-Curie Actions

  Award: 872931