Change Detection (CD) techniques applied over multitemporal multispectral or hyperspectral remote sensing images allow monitoring changes in the land use or catastrophe tracking, among other applications. A multiclass CD technique for multidimensional images that is robust in the presence of noise is presented in this paper. The technique combines fusion at feature level to perform a ﬁrst change/no change labeling (binary CD) and a later stage with fusion at decision level that performs a supervised multidate classiﬁcation of the changed pixels (multiclass CD) obtaining the ﬁnal from-to change map. The acquisition of multidimensional images usually corrupts the original signal by adding noise. This noise can be related with natural random processes or it can be produced during the sensor operation. Additive White Gaussian Noise (AWGN) and speckle noise simulate these eﬀects. In this paper the robustness of the proposed CD technique in noisy scenarios for these two types of noise of varying intensity is evaluated. The experimental results show that the proposed technique is more robust than other alternatives, achieving accuracies close to those obtained in the absence of noise. The proposed technique is designed to be eﬃciently computed in GPU, thus dealing with the high computational cost of the processing of multidimensional images.
Keywords: Multiclass change detection, remote sensing, noise, classiﬁcation, GPU