.png){kind=icon}
Return
Benchmark for Hyperspectral Unmixing Algorithm Evaluation
.png){kind=icon}
OA
AI
DOI：10.15388/23-INFOR522.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Over the past decades, many methods have been proposed to solve the linear or non-linear mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of hyperspectral imaging, each image pixel may contain spectra from multiple materials. In turn, hyperspectral unmixing is finding these materials and their abundances. A few main approaches to performing hyperspectral unmixing have emerged, such as nonnegative matrix factorization (NMF), linear mixture modelling (LMM), and, most recently, autoencoder networks. These methods use dif-ferent approaches in finding the endmember and abundance of information from hyperspectral im-ages. However, due to the huge variation of hyperspectral data being used, it is difficult to determine which methods perform sufficiently on which datasets and if they can generalize on any input data to solve hyperspectral unmixing problems. By trying to mitigate this problem, we propose a hyper -spectral unmixing algorithm testing methodology and create a standard benchmark to test already available and newly created algorithms. A few different experiments were created, and a variety of hyperspectral datasets in this benchmark were used to compare openly available algorithms and to determine the best-performing ones.
Keywords:
hyperspectral unmixing
benchmark
matrix factorization
autoencoders
linear mixture models
Journal
IF:
2.8
Papers:
402
Citations:
1.0K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
View more