A review of spatial enhancement of hyperspectral remote sensing imaging techniques
Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
Spectral unmixing via data-guided sparsity
Hyperspectral unmixing, the process of estimating a common set of spectral bases and their
corresponding composite percentages at each pixel, is an important task for hyperspectral …
corresponding composite percentages at each pixel, is an important task for hyperspectral …
Self-paced nonnegative matrix factorization for hyperspectral unmixing
The presence of mixed pixels in the hyperspectral data makes unmixing to be a key step for
many applications. Unsupervised unmixing needs to estimate the number of endmembers …
many applications. Unsupervised unmixing needs to estimate the number of endmembers …
Hyperspectral unmixing based on dual-depth sparse probabilistic latent semantic analysis
This paper presents a novel approach for spectral unmixing of remotely sensed
hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the …
hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the …
Hyperspectral unmixing using orthogonal sparse prior-based autoencoder with hyper-Laplacian loss and data-driven outlier detection
Hyperspectral unmixing, which estimates end-members and their corresponding abundance
fractions simultaneously, is an important task for hyperspectral applications. In this article …
fractions simultaneously, is an important task for hyperspectral applications. In this article …
An improved multiobjective discrete particle swarm optimization for hyperspectral endmember extraction
Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a
multiobjective optimization perspective, this task is extremely challenging because …
multiobjective optimization perspective, this task is extremely challenging because …
Endmember independence constrained hyperspectral unmixing via nonnegative tensor factorization
JJ Wang, DC Wang, TZ Huang, J Huang… - Knowledge-Based …, 2021 - Elsevier
Hyperspectral unmixing is an essential step for the application of hyperspectral images
(HSIs), which estimates endmembers and their corresponding abundances. In recent …
(HSIs), which estimates endmembers and their corresponding abundances. In recent …
Joint selection of essential pixels and essential variables across hyperspectral images
An approach is proposed and illustrated for the joint selection of essential samples and
essential variables of a data matrix in the frame of spectral unmixing. These essential …
essential variables of a data matrix in the frame of spectral unmixing. These essential …
Spectral–spatial robust nonnegative matrix factorization for hyperspectral unmixing
R Huang, X Li, L Zhao - IEEE Transactions on Geoscience and …, 2019 - ieeexplore.ieee.org
Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed
hyperspectral data, which aims to estimate a set of spectral signatures, called endmembers …
hyperspectral data, which aims to estimate a set of spectral signatures, called endmembers …
Simultaneously counting and extracting endmembers in a hyperspectral image based on divergent subsets
Most existing endmember extraction techniques require prior knowledge about the number
of endmembers in a hyperspectral image. The number of endmembers is normally estimated …
of endmembers in a hyperspectral image. The number of endmembers is normally estimated …