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Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiaoxiang Zhu. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing, IEEE TIP, 2019.
Resource collection for the paper "Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing" (SPM 2023).
Q. Yuan, Q. Zhang, J. Li, H. Shen, and L. Zhang, "Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network," IEEE TGRS, 2019.
A repository for methods by and implemented by the authors' that were used in the paper: Blind Hyperspectral Unmixing using Autoencoders: A Critical Comparison
This repository contains several hyperspectral image analysis algorithms, including unmixing, registration and fusion.
codes for the paper "Model-based deep autoencoder networks for nonlinear hyperspectral unmixing", IEEE GRSL 2021
Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images & Spatial Purity based Endmember Extraction for Spectral Mixture Analysis
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
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