Increasing soft classification accuracy through the use of an ensemble of classifiers

Abstract

Although soft classification analyses can reduce problems such as those associated with mixed pixels that impact negatively on conventional hard classifications their accuracy is often low. One approach to increasing the accuracy of soft classifications is the use of an ensemble of classifiers, an approach which has been successful for hard classifications but rarely applied for soft classifications. Four methods for combining soft classifications to increase soft classification accuracy were assessed. These methods were based on (i) the selection of the most accurate predictions on a class‐specific basis, (ii) the average of the outputs of the individual classifications for each case, (iii) the direct combination of classifications using evidential reasoning and (iv) the adaptation of the outputs to enable the use of a conventional (hard classification) ensemble approach. These four approaches were assessed with classifications of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High‐Resolution Radiometer (AVHRR) imagery of Australia. The data were classified using two neural networks and a probabilistic classifier. All four ensemble approaches applied to the outputs of these three classifiers were found to increase classification accuracy. Relative to the most accurate individual classification, the increases in overall accuracy derived ranged from 2.20% to 4.45%, increases that were statistically significant at 95% level of confidence. The results highlight that ensemble approaches may be used to significantly increase soft classification accuracy.

Acknowledgments

The authors wish to thank the USGS – NASA Distributed Active Archive Center at the Goddard Space Flight Center, Greenbelt for producing the NOAA AVHRR and IGBP DISCover data in their present form and distributing them. The original data products were produced under the NOAA/NASA Pathfinder AVHRR Land (PAL) program. The authors are also grateful to the Vietnamese Government for the provision of a scholarship to support HTXD study at the University of Southampton while on leave from Hanoi University of Mining and Geology, Vietnam. We are grateful to the reviewers for their helpful comments on the original submission.