In this paper, a novel graph matching algorithm, called Filtering Bi-directional K-Nearest-Neighbors Strategy (Filtering BiKNN Strategy) is presented to solve the pseudo isomorphic graph matching for remote sensing images with large affine transformation, similar patterns or from multisource sensors. BiKNN was proposed to describe the adjacent relationships of feature points. Filtering strategy is used to eliminate dubious matches of pseudo isomorphism for restrict constraints. Any BiKNN vertices of candidate outliers treated as outliers in latter iterations are rechecked with the expanded BiKNN respectively. Candidate outliers with stable graph structures are recovered to the residual sets. Three typical remote sensing images and twenty image pairs were utilized to evaluate the performance. Compared with random sample consensus (RANSAC), graphing transformation matching (GTM) and the proposed BiKNN matching, Filtering BiKNN Strategy can deal with pseudo isomorphism and obtain the highest recall and precision.