DISCOVER PROJECT
Satellite Image Processing
From Raw Pixels to Insights – Multispectral Satellite Image Analysis and Enhancement
Master’s level project applying remote sensing and statistical image processing techniques to analyze multispectral satellite imagery, focusing on band-wise exploration, visualization, and contrast enhancement for improved interpretability.
PYTHON - NUMPY - RASTERIO - MATPLOTLIB
Project Overview
The project involves loading geospatial raster data, exploring its structure, and performing detailed band-wise analysis. Each spectral band is processed independently to compute statistical measures such as mean, range, and standard deviation. Histogram visualization is used to understand pixel intensity distribution, followed by percentile-based rescaling to enhance contrast. This pipeline ensures both quantitative and visual understanding of satellite imagery.
The objective of this project is to analyze multispectral satellite images by extracting individual spectral bands (Blue, Green, Red, NIR), computing their statistical properties, and enhancing their visual quality through contrast stretching. The goal is to improve data interpretability for further applications such as vegetation analysis, land cover classification, and remote sensing insights.
