Remote Sensing Data Analysis in R 🛰

List of resources for mineral exploration and machine learning, generally with useful code and examples.

Spectral endmembers and unmixing tools for satellite land cover mapping.

Processing of HSIs: spectral unmixing and classification.

[ICCV 2025] UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields

MLNMF: Multilayer Nonnegative Matrix Factorization

Pipeline for remotely sensed imagery. The pipeline processes satellite imagery alongside auxiliary data in multiple steps to arrive at a set of trend files related to land-cover changes.

Data and MATLAB code for the simulations of [R. Arablouei, “Spectral unmixing with perturbed endmembers,” submitted to the IEEE Transactions on Geoscience and Remote Sensing, 2017.]

Decoding and analysis software for MRBLEs (Microspheres with Ratiometric Barcode Lanthanide Encoding).

This toolbox allows the implementation of the Diffusion and Volume maximization-based Image Clustering algorithm for unsupervised hyperspectral image clustering. See "README.md" for more information. Copyright: Sam L. Polk, 2023.

Analysis of the reflectance spectra from paintings: classification and endmembers.

Plate-forme Python modulaire pour l’analyse et le démélange spectral semi-supervisé d’images hyperspectrales HySpex (jeu de données DLR HySU). Elle combine : un pipeline NMF pour extraire endmembers + abondances ; un VAE convolutionnel semi-supervisé pour la classification de patchs ; des outils de pré-traitement et visualisation (RMSE, SAD, SID

Classifying the materials of individual pixels taken by satellite using Spectral Unmixing and Pixel Classification.

A high resolution tool for snow cover reconstruction studies

compare two-point UV/Vis spectroscopy read-out with spectral unmixing

It is possible to predict the spectrum of e.g. a nucleobase in a nucleoside-nucleobase conversion.

My semester project for the course 'Machine Learning and Computational Statistics' during my studies in MSc in Data Science, AUEB.