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

ABSTRACT

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 atmospheric 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.

Acknowledgments

The authors would like to acknowledge the ANR HYEP for the financial support of this work, the Onera institute (and especially Philippe Deliot and Laurent Poutier) for the provision of the hyperspectral data and the advices regarding COCHISE, and the IGN institute (especially Arnaud Le Bris) for the coregistration of the two Hyspex sensors.

Disclosure statement

No potential conflict of interest was reported by the authors.