A high‐level data fusion and spatial modelling system for change‐detection analysis using high‐resolution airborne digital sensor data

Abstract

A high‐level data fusion system that uses Bayesian statistics involving weights‐of‐evidence modelling is described to combine disparate information from airborne digital data such as digital surface model (DSM), colour, thermal infrared (TIR) and hyperspectral images at different time periods. To determine the efficacy of the system, an analysis of change detection was performed. The data fusion system is capable of detecting changes in man‐made features automatically in a densely populated area where there is little prior information. Multiclass segmented images were obtained from the data captured by four airborne remote sensing sensors. The system performs data fusion modelling by using binary images of each theme class and a total of 40 binary patterns were obtained. Through Bayesian methods, involving weights‐of‐evidence modelling, all the binary images were analysed and finally four binary patterns (indicator images) were identified automatically as significant for the change‐detection application. A weighted index overlay model available in the system combines these four patterns. Data fusion by weights‐of‐evidence modelling is found to be straightforward and unequivocal for predicting newly transformed locations. The results of the Bayesian method are accurate as the weights are based on statistical analysis. Changes in features such as colour of roofs, parking areas, openland areas, newly built structures, and the presence or absence of vehicles are extracted automatically by using the high‐level data fusion approach. The final predictor image shows the probability of change‐detected areas in a densely populated city in Japan.