A New Elbow Estimation Method for Selecting the Best Solution in Sparse Unmixing
The goal of hyperspectral image analysis is often to determine which materials, out of a given set of possibilities, are present in each pixel. As hyperspectral data are being gathered in rapidly increasing amounts, automatic image analysis is becoming progressively more important. Automatic identif...
Main Authors: | Risto Sarjonen, Tomi Raty |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10105448/ |
Similar Items
-
Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability
by: Tatsumi Uezato, et al.
Published: (2020-07-01) -
Unmixing of Hyperspectral Data Using Spectral Libraries
by: Sefa Küçük, et al.
Published: (2020-04-01) -
Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing
by: Pan Zheng, et al.
Published: (2021-01-01) -
Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems
by: C.V.S.S. Manohar Kumar, et al.
Published: (2022-08-01) -
Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation
by: Elham Kordi Ghasrodashti, et al.
Published: (2017-05-01)