A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations

ABSTRACT

Combining optical and polarimetric synthetic aperture radar (PolSAR) earth observations offers a complementary data set with a significant number of spectral, textural, and polarimetric features for crop mapping and monitoring. Moreover, a temporal combination of both sources of information may lead to obtaining more reliable results compared to the use of single-time observations. In this paper, an operational framework based on the stacked generalization of random forest (RF), which efficiently employed bi-temporal observations of optical and radar data, was proposed for crop mapping. In the first step, various spectral, vegetation index, textural, and polarimetric features were extracted from both data sources and placed into several groups. Each group was classified separately using a single RF classifier. Then, several additional classification tasks were accomplished by another RF classifier. The earth observations used in this paper were collected by RapidEye satellites and the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system over an agricultural region near Winnipeg, Manitoba, Canada. The results confirmed that the proposed methodology was able to provide a higher overall accuracy and kappa coefficient than traditional stacking method, and also than all the individual RFs using each group. These accuracy metrics were also better than those of the RFs using the stacked features. Moreover, only the proposed methodology could achieve standard accuracy (F-score ≥85%) for all crop types in the study area. The visual comparison also demonstrated that the crop maps produced by the proposed methodology had more homogeneous, uniform appearances. Moreover, the mixed pixels of crop types, which abundantly existed in the traditional stacking and individual RFs̕ maps, were significantly eliminated.

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Acknowledgments

The authors would like to present their acknowledgments to the JPL NASA for the PolSAR images, and the SMAPVEX 2012 team, the Agriculture and Agri-Food Canada, and Dr. S. Homayouni, from the Dept. of Geography, Environment, and Geomatics of the University of Ottawa, Canada, for providing for the PolSAR and the optical and fields survey data used in this research. This research was partially supported by the Iranian National Elites Foundation.

Disclosure statement

No potential conflict of interest was reported by the authors.