Mapping vegetation and land cover in a large urban area using a multiple classifier system

Abstract

ABSTRACTVegetation and land-cover information is critical for sustainable environmental management in urban areas. Remote sensing has increasingly been used to derive such information, yet it has been challenged by the spectral and spatial complexity in the urban environment. In this study, we developed a multiple classifier system (MCS) to help improve remote-sensing-based vegetation and land-cover mapping in a large metropolitan area. MCSs, although considered as an emerging hot topic and a promising trend in pattern recognition, have not received the attention it deserves in the remote-sensing community. Our work consisted of several components. First, we identified a group of commonly used pattern recognizers from different families of statistical learning algorithms as base classifiers. Then, we implemented them to derive land-cover information from a satellite image covering the study site. Last, we adopted a weighting and combination method to generate the final map. Results indicate that there is statistically significant difference in the classification accuracy between the MCS developed and each base classifier considered. Comparing with the base classifiers, the MCS produced not only about 5–8% higher overall classification accuracy but also the most stable categorical accuracies. Moreover, the MCS generated a larger accuracy improvement for spectrally complex classes than for relatively homogenous ones, suggesting its comparative advantage in reducing classification errors caused by class ambiguity. The novelties of our work are with the demonstration of how MCSs can be operationally used to improve image classification from large remote sensor data sets with complex patterns and with the insight into the behaviour of MCSs in relation to the complexity of individual classes. These findings can help promote the use of MCSs as an emerging premier approach for image classification by the remote-sensing community.