This paper introduces a heterogeneous image registration algorithm called Extended Phase Consistency Spatial Relationship Constraints （EPC-SRC）. The objective is to address challenges related to a high mismatching rate and low registration accuracy encountered in registering heterogeneous images. Initially， we present a chunked Harris feature detection method based on an adaptive weighted moment map. This method aims to identify stable， uniformly distributed， and repeatable key feature points. Subsequently， a multiscale weighted maximum index map （MSW-MIM）， combined with an enhanced GLOH histogram-like （GLOH-like） approach， is employed to construct descriptors. This strategy enhances the distinguishability of descriptors， contributing to improved accuracy. Finally， the Marginalizing Sample Consensus （MAGSAC） method is utilized to establish corresponding points. This facilitates precise feature point matching by incorporating spatial relationship constraints. An iterative approach is employed to solve the homography matrix， resulting in high-precision image registration. A comparative analysis involving ten experiments on aligning heterogeneous remote sensing images demonstrates that our proposed method significantly outperforms other algorithms. Notably， our approach attains superior results in terms of matching accuracy.