A Multitemporal Remote Sensing Image Registration Method Based on Water Bodies for the Lake-Rich Region

Abstract

Accurate multitemporal remotely sensed image registration is essential for water body change detection and analysis of lake dynamics. However, lake-rich regions, such as Siberia, are mostly dominated by rivers and lakes, with few stable geometry features such as those typically used as control points for image registration. Lakes in Arctic regions are generally not static; their shorelines tend to expand and shrink seasonally, and may change substantially between different years, making it difficult to find consistent features for image registration. Consequently, traditional image-to-image registration methods, and even many sophisticated registration algorithms, rarely achieve accurate geometric correction in Arctic regions due to a lack of sufficient control points. In this article, we proposed a summary lake spatial attribute, the inferred deepest point of the lake (DPL), as a feature that is relatively insensitive to lake area changes, and therefore useful for registration of multitemporal images. The central focus of the DPL estimation algorithm is the identification of the largest inner circle (LIC) of the lake polygon. First, the internal Voronoi diagram of a lake polygon is calculated by the ``divide-and-conquer” method. The medial axis (MA) is then calculated by Voronoi diagram simplification, and finally, the LIC center is obtained by computing the distance from the MA intersection to all polygon edges. The approach was used to register HJ-1 and Landsat multispectral scanner (MSS) images in Siberia, where water bodies dominate the landscape and change significantly over time. The proposed method found a large number of control points from the extracted water bodies. Subpixel registration accuracy of 0.62 pixels (18.5 m) and 0.33 pixels (19.6 m) root mean square error (RMSE) was obtained for the HJ-1 and MSS images, respectively. In comparison, the alternative method of using lake centroids, only achieved 0.75 pixels (22.4 m) and 0.49 pixels (29.3 m) RMSE. This registration accuracy improvement is potentially important for large-scale regional cartography and change detection applications.