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
Latest papers with no code
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner.
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery
The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0. 997 and 0. 990, respectively, whereas the classical approach achieved only 0. 93 and 0. 82.
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing
Drawing inspiration from this, we propose a novel attention based Hyperspectral Unmixing algorithm called Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing (FusionNet).
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning
In fact, most nonlinear unmixing methods are designed by assuming specific assumptions on the nonlinearity model which subsequently limits the unmixing performance.
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability
Our multilayer model is based on the postulate that if we arrange the varied endmembers as an expanded endmember matrix, that matrix exhibits a low-rank structure.
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing
Spectral unmixing is a significant challenge in hyperspectral image processing.
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing
The Hyperspectral Unxming problem is to find the pure spectral signal of the underlying materials (endmembers) and their proportions (abundances).
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising
In this way, both the characteristics of the deep autoencoder based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance.
SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images
To this end, we put forward a spatial attention weighted unmixing network, dubbed as SAWU-Net, which learns a spatial attention network and a weighted unmixing network in an end-to-end manner for better spatial feature exploitation.
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders
Current deep learning-based nonlinear unmixing focuses on the models in additive, bilinear-based formulations.
