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.

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

30 Aug 2017pmelchior/proxmin

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.

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# A Gaussian mixture model representation of endmember variability in hyperspectral unmixing

29 Sep 2017zhouyuanzxcv/Hyperspectral

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).

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# Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization

22 Jan 2014neel-dey/robust-nmf

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

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# EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

6 Aug 2017savasozkan/endnet

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

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

3 Aug 2018savasozkan/dscn

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.

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# Deep Spectral Convolution Network for HyperSpectral Unmixing

22 Jun 2018savasozkan/dscn

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

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

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

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

18 Jun 2020zbzhzhy/PZRes-Net

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.

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

2 Nov 2018talesimbiriba/ULTRA-V

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.

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# Hyperspectral Unmixing Using a Neural Network Autoencoder

22 Mar 2018dv-fenix/HyperspecAE

Also, deep encoders are tested using different activation functions.

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