Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve class...

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Detalles Bibliográficos
Autores principales: Liu, Hong, Xia, Kewen, Li, Tiejun, Ma, Jie, Owoola, Eunice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472477/
https://www.ncbi.nlm.nih.gov/pubmed/32784692
http://dx.doi.org/10.3390/s20164413

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472477/
https://www.ncbi.nlm.nih.gov/pubmed/32784692
http://dx.doi.org/10.3390/s20164413

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