Land Cover Classification using Multitemporal CHRIS/PROBA Images and Multitemporal Texture

Most existing multitemporal classification researches use spectral information alone. However, adding spatial structure and temporal correlation in the classification could improve the classification accuracy. This paper proposed a new method to extract multitemporal texture by the Pseudo Cross Variogram (PCV). The derived texture features were combined with the original spectral information for multitemporal classification. The performance of the proposed multitemporal texture was evaluated in land cover classification using bi-temporal hyperspectral CHRIS/PRBOA images. The experiments showed that CHIRS/PROBA data is applicable in multitemporal classification, and including multitemporal texture in multitemporal classification could lead to a significant increase in overall classification accuracy, compared to the classification using spectral information alone.