Automated Image Registration Based on Pseudoinvariant Metrics of Dynamic Land-Surface Features

Accurate assessment of land-cover/land-use change is essential for understanding the impacts of global change and necessitates the use of satellite data. Satellite change detection requires large volumes of multitemporal images to be precisely registered. Image registration is particularly difficult in dynamic (i.e., rapidly time varying) landscapes since the changes themselves interfere with the process of tie-point identification. Despite the existence of sophisticated registration algorithms, it is still problematic to register images acquired over such areas due to a dearth of stable features. Hence, we propose an automated image registration method using tie points derived from pseudoinvariant features (PIFs) and apply the method to register satellite images for hydrologic change detection in the Arctic, where abundant shallow lakes dominate the landscape but change significantly over time. A key to the method is the identification of "shape-stable" lakes as PIFs, which preserve their geometric shape even though the shorelines may migrate significantly. The proposed method automatically identifies PIFs based on scale-invariant shape descriptors and employs their center points for establishing the registration model. Our method thus consists of water-body feature extraction, PIF detection based on feature shape criteria, and image registration using tie points derived from the PIFs. The approach is used to register 1978 and 2000 Landsat images in Alaska, where lakes dominate the landscape and change significantly over time. The performance of the proposed approach is evaluated quantitatively, and a high subpixel registration accuracy of 0.66 pixel at Enhanced Thematic Mapper Plus resolution (i.e., 19 m) is achieved. A comparative evaluation indicates that the proposed approach outcompetes the conventional manual tie-point selection method and automated image registration techniques based on fast Fourier transform.