Adaptive Regional Multiple Features for Large-Scale High-Resolution Remote Sensing Image Registration

Abstract

The efficient and accurate registration of multitemporal images is essential for many remote sensing applications. With the increase in the imaging resolution and field in satellites, the acquired large-scale remote sensing images have brought serious challenges, since there exist parallax shifts and large background variations among different local regions of the acquired images. To address these issues, this article proposes an adaptive regional multiple features (ARMF) matching method for the registration of multitemporal large-scale high-resolution remote sensing images. Specifically, since large background variations in fixed-size regions of multitemporal images will cause insufficient features and the failure of features matching, the ARMF introduces an adaptive regions searching strategy, which utilizes the pyramid amplification technique to adaptively select the regions that can find the sufficient matched features. Then, the ARMF extracts multiple types of features (i.e., gradient feature, phase feature, and line feature) from the adaptive searched region that can more effectively represent the characteristics of the large regions. Finally, we utilize the feature matching error as the rule to adaptively select the suitable features as the descriptors of the region. The experimental results on large-scale multitemporal image data obtained from Google Earth demonstrated the proposed method can outperform several state-of-the-arts remote sensing registration approaches.