Robust Hyperspectral Unmixing in the Presence of Mixed Noise using Joint Sparsity and Total Variation Regularization
Main Article Content
Abstract
Hyperspectral unmixing is a vital process in hyperspectral imaging, aiming to estimate constituent endmembers and their fractional abundances within each pixel. Hyperspectral images are often marred by various noise sources. This research delves into the hyperspectral unmixing challenge in a general scenario that accounts for mixed noise, explicitly addressing Gaussian noise and sparse noise. The unmixing model is tailored to harness the joint sparsity of abundance maps, enabling the robust separation of endmembers and abundances from noise. Additionally, a total-variation-based regularization technique is incorporated to capture the smoothness of abundance maps, further enhancing the accuracy of the unmixing process in noisy hyperspectral images.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.