A Parallel Positive Boolean Function approach to supervised multispectral image classification

In this paper, we present a parallel computing technique, referred to as parallel positive Boolean function (PPBF), for supervised classification of multispectral images. The approach is based on the generalized positive Boolean function (GPBF) scheme, which has been successfully applied in multispectral image classification. The GPBF classifier is developed from a stack filter. The stack filter is defined as the class of all nonlinear digital filters. Each stack filter corresponding to a GPBF possesses the weak superposition property and the ordering property. In order for the GPBF to be effective, the proposed PPBF is performed to improve the computational speed by using parallel cluster computing techniques. It creates a set of stack filters in each parallel node implemented by message passing interface (MPI). The proposed PPBF technique reduces the structure complexity of original GPBF. The effectiveness of the proposed PPBF is evaluated by fusing Systeme Pour l’Observation de la Terre (SPOT) images and digital elevation model (DEM) information for land cover classification during the post 921 Earthquake period in Taiwan. The experimental results demonstrated that PPBF not only significantly improves the computational loads of GPBF classification, but also substantially improves the precision of classification compared to conventional classification.