Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification.
Sci Rep
; 14(1): 19308, 2024 Aug 20.
Article
em En
| MEDLINE
| ID: mdl-39164343
ABSTRACT
This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Chile
País de publicação:
Reino Unido