Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue International Journal of Remote Sensing Année : 2017

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Résumé

For remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmo-spheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial resolution where COCHISE can be 10% more accurate than the empirical method.
Fichier principal
Vignette du fichier
Roussel2017.pdf (565.69 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01888019 , version 1 (26-05-2021)

Licence

Paternité

Identifiants

Citer

Guillaume Roussel, Christiane Weber, Xavier Briottet, Xavier Ceamanos. Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution. International Journal of Remote Sensing, 2017, 39 (5), pp.1593-1614. ⟨10.1080/01431161.2017.1410247⟩. ⟨hal-01888019⟩
102 Consultations
92 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More