Heterogeneous remote sensing (HRS) image fusion effectively improves image interpretability to better achieve the subsequent target detection tasks. Current HRS image fusion methods own limited target improvement performance leading to degraded target detection performance because they mainly focus on enhancing the geometric structure and texture details. In this paper, a novel homogeneous transformation and target enhancement (HTTE) module-based HRS image fusion method is designed. In HTTE, deep homogeneous feature fusion is used to transform HRS images into homogeneous images with similar image styles and then the proposal-copula-based target enhancement strategy is utilized to fuse homogeneous images, enhance target information, and relatively suppress background clutter therein. Experiments using measured high-resolution spaceborne and airborne inshore HRS data show that our proposed HTTE-based method improves the image fusion quality in comparison with current commonly used methods.