Cargando…
A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification
Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hy...
Autores principales: | Ramirez Rochac, Juan F., Zhang, Nian, Thompson, Lara A., Deksissa, Tolessa |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279854/ https://www.ncbi.nlm.nih.gov/pubmed/34306058 http://dx.doi.org/10.1155/2021/9923491 |
Ejemplares similares
-
Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine
por: Wang, Ke-Fan, et al.
Publicado: (2022) -
Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification
por: Datta, Debaleena, et al.
Publicado: (2022) -
A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis
por: Wankmüller, Sandra
Publicado: (2022) -
Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention
por: Nguyen, Viet Dung, et al.
Publicado: (2022) -
Deep learning applied to hyperspectral endoscopy for online spectral classification
por: Grigoroiu, Alexandru, et al.
Publicado: (2020)