Hyperspectral Unmixing
26 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
Benchmarks
These leaderboards are used to track progress in Hyperspectral Unmixing
Most implemented papers
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints
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.
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework.
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
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
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
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
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
Also, deep encoders are tested using different activation functions.
Deep Spectral Convolution Network for HyperSpectral Unmixing
savasozkan/dscn
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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
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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
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.
