Hyperspectral Unmixing
23 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
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.
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques.
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process.
Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization
Second, existing methods do not explicitly account for the effects of stripe noise, which is common in HS measurements, in their formulations, resulting in significant degradation of unmixing performance when such noise is present in the input HS image.
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.
Deep Hyperspectral Unmixing using Transformer Network
In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep unmixing model with transformers.
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components.
Smoothed Separable Nonnegative Matrix Factorization
More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point to each vertex.