期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B2
页码：445-450
出版社：Copernicus Publications
摘要：Change detection is the process of identifying differences in the state of objects and/or phenomena under consideration by observing them at different times. Change detection is important for monitoring and managing natural resources, urban development, environmental changes, and disaster assessments. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, allow for reliable identification and prediction of change patterns. The quality of the image registration process of the involved imagery is the key factor that dictates the validity and the reliability of the change detection outcome. The fact that change detection analysis might involve multi-spectral, multi-source, and/or multi-resolution imagery captured at different times calls for the development of a robust registration procedure that can handle these types of imageries. This paper introduces a new approach for semi-automatic image registration using linear features, which can be reliably extracted from imagery with significantly different geometric and radiometric properties. The Modified Iterated Hough Transform (MIHT) is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function relating the images to be registered as well as the correspondence between conjugate lines. Once the registration problem has been solved, the suggested methodology proceeds by detecting changes between the registered imagery. Traditional change detection methodologies, which are based on the subtraction of intensity images, usually fail due to different illumination conditions, sensors, and/or viewing perspectives at the moments of exposure. To overcome these problems, features that are invariant to changes in the illumination conditions can be used. Based on this reasoning, derived edges from the registered images are used as the basis for change detection in this research. Experimental results using real data proved the feasibility of the suggested approach for deriving a quantitative estimate of changes among the registered images
