Cargando…
SLIC Superpixel-Based l(2,1)-Norm Robust Principal Component Analysis for Hyperspectral Image Classification
Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become a...
Autores principales: | Zu, Baokai, Xia, Kewen, Li, Tiejun, He, Ziping, Li, Yafang, Hou, Jingzhong, Du, Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386951/ https://www.ncbi.nlm.nih.gov/pubmed/30682823 http://dx.doi.org/10.3390/s19030479 |
Ejemplares similares
-
Robust Superpixel Segmentation for Hyperspectral-Image Restoration
por: Fan, Ya-Ru
Publicado: (2023) -
Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC
por: Wang, Yu, et al.
Publicado: (2020) -
A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
por: Zou, Huanxin, et al.
Publicado: (2016) -
Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification
por: Wang, Nanlan, et al.
Publicado: (2022) -
Hyperspectral Image Classification with Spatial Filtering and ℓ(2,1) Norm
por: Li, Hao, et al.
Publicado: (2017)