This project focuses on visualizing and analyzing remotely sensed data of the Bullialdus Crater on the Moon, sourced from Chandrayaan-1's Moon Mineralogy Mapper (M3). The dataset, provided in .tif format, is processed to explore potential water distribution and mineralogical features within the crater.
Bullialdus Crater, located in the lunar near side's southern highlands, is a site of interest due to its mineral-rich composition, including pyroxenes and feldspars. The crater's geologic diversity makes it a prime candidate for studying water retention and lunar evolution.
- Process and analyze high-resolution spectral data of Bullialdus Crater.
- Apply clustering and classification algorithms to identify water-rich regions.
- Create engaging and interactive visualizations to represent the findings.
The data used in this project comes from the Moon Mineralogy Mapper (M3), which provided multi-band spectral imaging of the Moon during Chandrayaan-1’s mission. Specifically, the dataset focuses on the Bullialdus Crater, a region of interest for its unique mineralogical composition and potential traces of water.
Dataset: Kaggle(https://www.kaggle.com/datasets/yash92328/lunar-water-discovery)
-
Data Preprocessing:
- Import
.tiffiles and clean data for noise and redundancy. - Extract spectral bands of interest for water detection.
- Import
-
Analysis:
- Apply clustering algorithms (e.g., k-means) to group regions based on mineralogical and water content signatures.
- Perform statistical analysis to confirm patterns.
-
Visualization:
- Generate 2D and 3D plots for spectral band intensity across the crater.
- Highlight water-rich regions using color-coded maps.
- Programming Language: Python
- Libraries: Rasterio, NumPy, Pandas, Matplotlib, Seaborn, SciKit-Learn
- Visualization Frameworks: Plotly
The analysis aims to reveal water-rich zones and unique mineralogical features within Bullialdus Crater, contributing to a deeper understanding of lunar resources and geologic history.