Object-Oriented Classification for Change Detection with Different Spatial Resolution Images

Abstract

Aerial photos have been increasingly and commonly used in various spatially related applications. Many municipalities and government agencies have constructed aerial photo databases all over the world. Keeping these databases up to date is the most important part of making them effective so that aerial photo databases are expected to be updated as frequently as possible. However, in practice, some of them are barely updated because of high cost. In this study, medium spatial resolution imagery is proposed to detect changes. Instead of using aerial photos, free accessible Landsat ETM+ from GeoBase and orthophotomaps from SODB are used for change detection. In order to compare with different spatial resolution images orthophotomaps are decomposed, segmented, and classified through wavelet transform and object-oriented classification. Although the detected changes are rough, the result shows that the method is quite cost-effective and practical. Moreover, it could support decision making for updating aerial photo databases.