Super-Resolution of Lunar XRF Elemental Abundances

This repository contains code developed for super-resolution by deep spatial interpolation model of the mapping of lunar surface elemental abundances obtained from Chandrayaan-2 CLASS instrument XRF data. The detailed description of the project is avaiable in this report (https://drive.google.com/file/d/12J4m65JyD-cekRiMmBzjrCBWQKoxNWYq/view?usp=drive_link).

Project Structure

Main Components:

1. file_initialisation.py:

   • Initializes the 64 subregion CSV files for the Moon's surface.
   • For each subregion, computes the latitudes and longitudes of 2 km x 2 km pixels.
   • Extracts features from image tiles using a pre-trained ResNet50 model.
   • Populates the CSV files with selected features, mare/highland classifications, and metadata.

2. top_k_features.ipynb:

   • Analyzes feature importance from ResNet50 extractions.
   • Identifies the top 300 features for further processing based on variance thresholding.

3. Final.py:

   • Combines all parts of the project.
   • Constructs subgraphs for abundance maps.
   • Train the GNN-CNN combined model on these subgraphs.
   • Returns the final high resolution abundance maps.

Prerequisites :

• Python 3.10+
• GPU with CUDA support (optional but recommended).
• Clone the repository:

  git clone https://github.com/who-else-but-arjun/ISRO_XRF_SR.git
  cd ISRO_XRF_SR

• Install dependencies:

  pip install -r requirements.txt

• Shapefile for Mare Regions: LROC_GLOBAL_MARE_180.SHP (https://drive.google.com/file/d/12J4m65JyD-cekRiMmBzjrCBWQKoxNWYq/view?usp=drive_link).
• Lunar Surface Image: Lunar_LRO_LROC-WAC_Mosaic_global_100m_June2013.tif (https://planetarymaps.usgs.gov/mosaic/Lunar_LRO_LROC-WAC_Mosaic_global_100m_June2013.tif).
• CSV Files: Intermediate outputs from file_initialisation.py.

Place the downloaded files in their respective directories as outlined below:

• LROC_GLOBAL_MARE_180.SHP: ./Mare Classification/
• Lunar_LRO_LROC-WAC_Mosaic_global_100m_June2013.tif: Root directory.

.
├── file_initialisation.py                                   # Initialization and feature extraction.
├── top_k_features.ipynb                                     # Feature analysis and selection.
├── Final.py                                                 # Full pipeline integration.
├── Lunar_LRO_LROC-WAC_Mosaic_global_100m_June2013.tif                
├── requirements.txt                                         # Dependency file.
├── Mare Classification/                                     # Directory for shapefiles.
        | - LROC_GLOBAL_MARE_180.DBF
        | - LROC_GLOBAL_MARE_180.PRJ
        | - LROC_GLOBAL_MARE_180.SHP
        | - LROC_GLOBAL_MARE_180.SHP.XML
        | - LROC_GLOBAL_MARE_180.SHX
        | - LROC_GLOBAL_MARE_README.TXT  
├── PART2Output.npz                                           # Stores updated subregions
├── graphs/
├── masks/     
└── regions/                                                  # Outputs of file_initialisation.py.

Outputs:

• regions CSVs:

  • Files named subregion_<i>_<j>.csv.
  • Columns include:
    • lat_center and lon_center: Geographic coordinates of the pixel centers.
    • Elemental abundances: Features like Fe, Ti, Ca, etc.
    • mareOrHighland: Binary classification indicating whether the region is Mare or Highland.
    • Top 300 spatial features: Feature columns extracted from lunar rgb images and top 300 selected features based on variance thresholding.

• Graphs and their corresponding masks:

  • Structured inputs for graph neural network models.
  • Encodes spatial and feature-based relationships within subregions.
  • Graphs stored in graphs\graphs_subregion_<i>_<j>\subgraph_{i}_{j}_{row_idx}_{col_idx}.pt.
  • Masks for the graphs stored in masks\masks_subregion_<i>_<j>\mask_{i}_{j}_{row_idx}_{col_idx}.pt.

• Final High-Resolution Maps:

  • Enhanced elemental abundance data for each subregion.
  • Saved as updated subregion_<i>_<j>.csv files for mapping.

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Running the Project

Step 1: Run file_initialisation.py

This script initializes and populates the subregion CSV files.

python PART1.py

Step 2: Run the Final Code

The final.py script combines all components and generates the high-resolution abundance maps.

python Final.py --mode 1 <input_data.csv>

mode = 1 for population of initialised csv files with the input elemental abundances data.

python Final.py --mode 2 <i> <j> <num_iterations>

mode = 2 for create the graphs for each subregion and training and interpolation for final high resolution abundnaces mapping.

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