Hyperspectral Unmixing

15 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

Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints

pmelchior/proxmin 30 Aug 2017

We introduce a generalization of the linearized Alternating Direction Method of Multipliers to optimize a real-valued function $f$ of multiple arguments with potentially multiple constraints $g_\circ$ on each of them.

Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization

neel-dey/robust-nmf 22 Jan 2014

This paper introduces a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures.

Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF

roozbehrajabi/mlnmf 12 Aug 2014

In this letter we proposed using multilayer NMF (MLNMF) for the purpose of hyperspectral unmixing.

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

savasozkan/endnet 6 Aug 2017

Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications.

A Gaussian mixture model representation of endmember variability in hyperspectral unmixing

zhouyuanzxcv/Hyperspectral 29 Sep 2017

We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives).

Hyperspectral Unmixing Using a Neural Network Autoencoder

dv-fenix/HyperspecAE 22 Mar 2018

Also, deep encoders are tested using different activation functions.

Deep Spectral Convolution Network for HyperSpectral Unmixing

savasozkan/dscn 22 Jun 2018

In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN).

Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty

savasozkan/dscn 3 Aug 2018

The results validate that the proposed method obtains state-of-the-art hyperspectral unmixing performance particularly on the real datasets compared to the baseline techniques.

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

talesimbiriba/ULTRA-V 2 Nov 2018

Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems.

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.