Hyperspectral Unmixing

23 papers with code • 0 benchmarks • 0 datasets

Hyperspectral Unmixing is a procedure that decomposes the measured pixel spectrum of hyperspectral data into a collection of constituent spectral signatures (or endmembers) and a set of corresponding fractional abundances. Hyperspectral Unmixing techniques have been widely used for a variety of applications, such as mineral mapping and land-cover change detection.

Source: An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Most implemented papers

Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning

zbzhzhy/PZRes-Net 18 Jun 2020

Specifically, PZRes-Net learns a high resolution and \textit{zero-centric} residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension.

Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model

johnjaniczek/InfraRender ECCV 2020

The dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented.

A Plug-and-Play Priors Framework for Hyperspectral Unmixing

xiuheng-wang/Plug_and_Play_HSI_unmixing 24 Dec 2020

Spectral unmixing is a widely used technique in hyperspectral image processing and analysis.

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

danfenghong/IEEE_TNNLS_EGU-Net 21 May 2021

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities.

Smoothed Separable Nonnegative Matrix Factorization

nnadisic/smoothed-separable-nmf 11 Oct 2021

More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point to each vertex.

Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing

hongmingli1995/ddica 7 Feb 2022

We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components.

Deep Hyperspectral Unmixing using Transformer Network

preetam22n/deeptrans-hsu 31 Mar 2022

In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep unmixing model with transformers.

Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing

xiuheng-wang/awesome-hyperspectral-image-unmixing 11 Jun 2022

Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.

Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing

inria-thoth/edaa 22 Sep 2022

In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.

Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization

mdi-tokyotech/towards_robust_hyperspectral_unmixing_mixed_noise_modeling_and_image-domain_regularization 16 Feb 2023

Second, existing methods do not explicitly account for the effects of stripe noise, which is common in HS measurements, in their formulations, resulting in significant degradation of unmixing performance when such noise is present in the input HS image.