Using projection pursuit learning network architecture to detect land use changes

Abstract

A robust method to conduct land use change detection between multi-temporal images using projection pursuit learning network architecture (PPLNA) is proposed. The method uses a parallel approach that includes three different PPLNs: two of them are used to generate the change map using the multi-spectral information, while the third produces a change mask exploiting multi-temporality. The distinctive feature and major merit of PPLNA from traditional neural network for land use change detection are the proposed method simultaneously exploits both the post classification of multi-spectral and multi-temporal information that is associated with the changes values of the pixel spectral reflectance, and hence improve the change detection accuracies. To validate the performance of the proposed method, the experiments using the ETM+ images for the area of Calgary have been carried out. The accuracies of the final classification and change detection maps have been evaluated with ground truth comparisons. The experimental result demonstrates that the proposed method achieves better accuracies. © 2008 SPIE.