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

MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

mhmdjouni/multihu-td-python 5 Oct 2023

Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework.

2
05 Oct 2023

Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing

ricardoborsoi/IDNet_release 3 Oct 2023

The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques.

3
03 Oct 2023

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

behnoodrasti/hysupp 18 Aug 2023

Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.

16
18 Aug 2023

Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks

ricardoborsoi/redsunn 19 Mar 2023

First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process.

4
19 Mar 2023

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.

0
16 Feb 2023

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.

9
22 Sep 2022

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.

69
11 Jun 2022

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.

33
31 Mar 2022

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.

5
07 Feb 2022

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.

0
11 Oct 2021