Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10662/20260
Títulos: | A single model CNN for hyperspectral image denoising |
Autores/as: | Maffei, Alessandro Haut Hurtado, Juan Mario Paoletti Ávila, Mercedes Eugenia Plaza Miguel, Javier Bruzzone, Lorenzo Plaza, Antonio |
Palabras clave: | Imágenes hiperespectrales;Sin ruido;Redes neuronales convolucionales;Información espacio-espectral;Hyperspectral images;Denoising;Convolutional neural networks;Spatial-spectral information |
Fecha de publicación: | 2020 |
Editor/a: | IEEE |
Resumen: | Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models-such as convolutional neural networks (CNNs)-to perform spectral-spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN. |
URI: | http://hdl.handle.net/10662/20260 |
ISSN: | 0196-2892 |
DOI: | 10.1109/TGRS.2019.2952062 |
Colección: | DTCYC - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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TGRS_2019_2952062.pdf | 11,03 MB | Adobe PDF | Descargar |
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