A k-Nearest Neighbor approach to improve change detection from remote sensing: Application to optical aerial images

This paper proposes a k-Nearest Neighbor (k-NN) based scheme in order to update a change detection decision from a Feed-Forward Neural Network (FFNN). Change and no-change detection is treated as a context-free binary classification problem, using a FFNN fed by pixel spectral intensity data. The particularity of this method, compared to the existing and established ones, is that it takes into account the result of change/no-change decision of neighboring pixels during the detection process. A first stage FFNN pixel-based classification is conducted and the output change/no-change label assigned to a pixel is updated via information from neighboring pixel labels. A majority vote strategy is adopted within the k-nearest neighbors' labels. Experiments are performed on real optical aerial images with large time differences. We show that the proposed system produces an overall performance detection improvement of around 13% and 4% of F-measure and G-mean values, respectively, over the FFNN baseline system.