Programming project of the course Machine Learning and Computational Statistics (MSc Data Science @ AUEB)
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Updated
Jun 24, 2024 - Jupyter Notebook
Programming project of the course Machine Learning and Computational Statistics (MSc Data Science @ AUEB)
A large and somewhat ugly plugin for viewing and analysing hyperspectral data in Napari
Simulator for Coded Aperture Spectral Snapshot Imaging
Easy and accessible repository to use codes to color images by wavelength value of HSI stacks and to modify a package to run succesfully the hsiPy Package for Phasor approach
An open source Python single-pixel imaging kit for educational and research purposes.
Comparative analysis of different feature extraction techniques for hyperspectral image classification.
biodivMapR: an R package for α- and β-diversity mapping using remotely-sensed images
Python library for hyperspectral analysis focused on spectroscopic approach.
This repository is dedicated to the segmentation of hyperspectral images during experimental animal surgery, where a variety of tasks were performed to process and analyze the hyperspectral data collected at the Institute of Image Guided Surgery in Strasbourg.
OL: Code for "Hyperspectral Image Super-resolution via Multi-stage Scheme without Employing Spatial Degradation"
Developing Low-Cost Multispectral Imagers using Inter-Band Redundancy Analysis and Greedy Spectral Selection in Hyperspectral Imaging.
spectralUI is an open source cross platform, general purpose tool for analyzing multispectral and hyperspectral images.
High performance data processing framework for hyperspectral image.
Single-Pixel Acquisition Software
Straightforward environment for hyperspectral analysis in Python.
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.
This toolbox allows the implementation of the following diffusion-based clustering algorithms on synthetic and real datasets.
Classification of Hyperspectral Images ( HSIs ) with Principal Component Analysis ( PCA ) in CUDA ( cuBLAS ).
Measuring the ripeness of fruit with Hyperspectral Imaging and Deep Learning
Classifying the materials of individual pixels taken by satellite using Spectral Unmixing and Pixel Classification.
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