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Hyperspectral Unmixing
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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
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Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis
Hyperspectral Image Generation with Unmixing Guided Diffusion Model
Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing
A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing
Spectral Unmixing Comparison with Sparse, Iterative and Mixed Integer Programming Models
Hyperspectral Unmixing using Iterative, Sparse and Ensambling Approaches for Large Spectral Libraries Applied to Soils and Minerals
DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing
Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture
Unrolling Plug-and-Play Network for Hyperspectral Unmixing
Theoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspective
Investigation of unsupervised and supervised hyperspectral anomaly detection
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework
An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing
Semi-NMF Regularization-Based Autoencoder Training for Hyperspectral Unmixing
Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorization
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability
Multi-Scale Convolutional Mask Network for Hyperspectral Unmixing
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
Hyperspectral Blind Unmixing using a Double Deep Image Prior
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising
SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders
Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.