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deephyin

UNSUPERVISED DEEP HYPERSPECTRAL INPAINTING

fasun

Fast Semisupervised Unmixing

funmix

Fast Unmixing Using Alternating Method of Multipliers

hapkecnn

Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network

hyftech-hyperspectral-shallow-deep-feature-extraction-toolbox

This Toolbox includes Hyperspectral Feature Extraction Techniques including Unsupervised, Supervised, and Deep Feature Extraction

hyminor

Hyperspectral Mixed Gaussian and Sparse Noise Reduction

hyperspectral-image-denoising-matlab-toolbox

This is hyperspectral image denoising Matlab toolbox contains 2D Wavelet denoising (3D Wavelet), 3D Wavelet Denoising (3D Wavelet), First Order Roughness Penalty DeNoising (FORPDN), and Hyperspectral Restoration (HyRes).

hyperspectral-image-denoising-toolbox-v2

This toolbox contains the following HSI denoising methods

hysupp

An Open-Source Hyperspectral Unmixing Python Package

hysure

HySURE is a technique for Hyperspectral Subspace Identification using SURE.

misicnet

MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing

optfus

OptFus: Optical Sensor Fusion For The Classification of Multi-source Data

otvca

Hyperspectral Feature Extraction Using Total Variation Component Analysis (OTVCA)

sslra

Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis

subfus

SubFus is a multisensor remote sensing image classification technique based on subspace sensor fusion.

sunaa

Sparse Unmixing using Archetypal Analysis

suncnn

Sparse Unmixing Using Unsupervised Convolutional Neural Network

undip

UnDIP: Hyperspectral Unmixing Using Deep Image Prior

unmixing_tutorial_ieee_iadf

Codes and data for Unmixing

wavelet-toolbox-wavelab_fast-

Wavelab_fast is a fast wavelet toolbox for one, two, and three dimensional signals. Wavelab_fast contains wavelet and undecimated wavelet transforms. Wavelet filters must be selected from wavelab toolbox by using MakeONFilter command or from Rice wavelet toolbox by using daubcqf command. Wavelab_fast is written based on modifying some codes from Wavelab and adding some codes for higher dimensional signals and implementing undecimated wavelet transform using algorithm a trous. The codes provided are much faster than the ones from wavelab for 2D and 3D signals. That has been done by skipping loops on pixels. ***The toolbox is recommended for applying on large 2D and 3D datasets. Also, in the case of having many 1D signals instead of using for loop.*** *** The code can only be used for academic purposes and the code must be cited by its DOI given by RG.*** *** To request for the password please send an email to [email protected]