Welcome to the Few-shot Satellite Image Classification (OPS-SAT) repository Follow the steps below to get started:

1. Clone the Repository:

   git clone https://github.com/ShendoxParadox/Few-shot-satellite-image-classification-OPS-SAT.git

2. Navigate to Repo Root Folder:

   cd Few-shot-satellite-image-classification-OPS-SAT

Make sure you have Conda installed on your machine.

# Create a virtual environment with Python 3.9
conda create --name myenv python=3.9

# Activate the virtual environment
conda activate myenv

# Install project dependencies
pip install -r requirements.txt

1. Build Docker Image:

   docker build --no-cache -t ops_sat:latest .

2. Run Docker Container:

   docker run -it ops_sat

3. Modify Configuration: Edit the config.json file as needed:

   nano config.json

4. Navigate to Source Folder:

   cd src/

5. Run OPS-SAT Development Script:

   python OPS_SAT_Dev.py

6. Choose W&B Option: Follow the prompts to choose the WandB option during script execution.

7. View Run Results: Navigate to the WandB dashboard to observe the run results.

8. (Another way) Pull and run the following docker image

   docker pull ramezshendy/ops_sat:latest
   docker run -it ramezshendy/ops_sat:latest

For any additional information or troubleshooting, refer to the documentation or contact the repository owner.

• Dataset Name: The OPS-SAT case dataset
• Dataset Variation Description: Augmented Color Corrected Synthetic Variation

• Training/Validation Dataset Path: ../Data/Variation_Synthetic_Generation_color_corrected_Augmentation/train/
• Test Dataset Path: ../Data/Variation_Synthetic_Generation_color_corrected_Augmentation/test/
  Change the path of the training and test datasets from the available dataset variations in the Data folder.

• Transfer Learning: false
  Means that the model will utilize pretraining using imagenet. If true, it will use transfer learning techniques.
• Transfer Learning Dataset: landuse
  The available transfer learning datasets are: landuse, imagenet, opensurfaces

• Project: OPS-SAT-Thesis-Project
• Input Shape: [200, 200, 3]
• Number of Classes: 8
• Dropout: 0.5
• Output Layer Activation: Softmax
• Model Optimizer: Adam
• Loss Function: FocalLoss
  The implemented loss functions to use from are: FocalLoss, SparseCategoricalCrossentropy
• Model Metrics: [SparseCategoricalAccuracy]
• Early Stopping:
  • Monitor: val_sparse_categorical_accuracy
  • Patience: 6
• Model Checkpoint:
  • Monitor: val_sparse_categorical_accuracy
• Cross Validation K-Fold: 5
• Number of Epochs: 200
• Batch Size: 4
• Focal Loss Parameters:
  • Alpha: 0.2
  • Gamma: 2
    If loss function is FocalLoss
• Number of Freeze Layers: 5
  If transfer learning is true.

• ESA OPS-SAT Competition
• Dataset on Zenodo

- /OPS-SAT-Thesis-Project
  - /Data
    - /Variation_Synthetic_Generation_color_corrected_Augmentation
      - /train
        - /Agricultural
        - /Cloud
        - /Mountain
        - /Natural
        - /River
        - /Sea_ice
        - /Snow
        - /Water
      - /test
    - /ops_sat
    - /Variation_Augmentation
    - /Variation_Original
    - /Variation_Synthetic_Generation
    - /Variation_Synthetic_Generation_color_corrected
  - /src
    - OPS_SAT_Dev.py
    - color_correction.py
    - image_augmentation.py
    - Your source code files
  - /notebooks
  - /models
    - best_weights.h5
    - fold_1_best_model_weights.h5
    - fold_2_best_model_weights.h5
    - fold_3_best_model_weights.h5
    - fold_4_best_model_weights.h5
    - fold_5_best_model_weights.h5
  - README.md
  - Dockerfile
  - config.json
  - .gitignore
  - requirements.txt

Class Accuracies

Correct Predictions

Wrong Predictions

Train - Val Accuracy

Train - Val Loss

System Charts

Run Info

Configuration Parameters

Model Files

Runs Parallel Plot