Skip to content

Latest commit

 

History

History
102 lines (88 loc) · 4.86 KB

README.md

File metadata and controls

102 lines (88 loc) · 4.86 KB

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 :

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.

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.