Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spect...
Guardado en:
Autor principal: | Fan Li |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Hindawi-Wiley
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/9769a5f40dfe4481b37ecb06ca823295 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
On the spectral nature of entanglement
por: Mario Mastriani
Publicado: (2021) -
Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
por: Ning Cui, et al.
Publicado: (2021) -
Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology
por: Qiang Zhao
Publicado: (2021) -
Research on the Ranked Searchable Encryption Scheme Based on an Access Tree in IoTs
por: Yan-Yan Yang, et al.
Publicado: (2021) -
Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
por: Weiyuan Tong, et al.
Publicado: (2021)