Land cover classification using CHRIS/PROBA images and multi-temporal texture

Abstract

Most existing multi-temporal classification studies use spectral information alone and ignore the temporal correlation between two-date images. This article proposes a new method to characterize the local temporal correlation using multi-temporal texture measured with a geostatistical function called the pseudo cross variogram (PCV). The derived multi-temporal texture, as an additional band, was combined with the spectral information in multi-temporal classification. The performance of the multi-temporal texture was evaluated and compared with the use of multi-temporal spectral data alone and plus the traditional variogram texture in land cover classification using bitemporal hyperspectral Compact High Resolution Imaging Spectrometer/Project for On Board Autonomy (CHRIS/PROBA) images. The results show that although land cover classification using spectral information from bitemporal CHRIS/PROBA data alone had an acceptable overall accuracy of 85.66%, the inclusion of multi-temporal texture in land cover classification led to significant increases (at the 95% confidence level) in both overall accuracy (3.3–4.3% improvement) and the kappa coefficient (4.9–6.6% improvement), particularly for vegetation classes. The incorporation of multi-temporal texture into multi-temporal land cover classification also outperformed the incorporation of the traditional variogram texture. The proposed method provides a new way to exploit the temporal correlation between bitemporal images for improved land cover classification.

Acknowledgements

This study is supported by National Science Foundation of China (NSFC) (grant number 40372130) and European Space Agency (ESA) Cat-1 Proba Project (grant number 3107). We sincerely thank the four anonymous reviewers for their constructive comments and suggestions, which greatly improved the quality of our article.