Using projection pursuit learning network architecture to detect land use changes

Bo Wu; Bo Huang; Yong Yan

doi:10.1117/12.812707

5 November 2008 Using projection pursuit learning network architecture to detect land use changes

Bo Wu, Bo Huang, Yong Yan

Proceedings Volume 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics; 71440H (2008) https://doi.org/10.1117/12.812707
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

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.

Citation Download Citation

Bo Wu, Bo Huang, and Yong Yan "Using projection pursuit learning network architecture to detect land use changes", Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71440H (5 November 2008); https://doi.org/10.1117/12.812707

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