Contour targets are affected by turbulence clutters in near-ground remote imaging scenes, leading to large matching errors. To address this problem, we propose a shape point set matching recognition method based on an oriented shape context and an edge continuity constraint. In the proposed method, directional features are embedded into a traditional shape context to construct a feature operator with a scale and rotation invariance. Further, inspired by the priori of edge continuity between the template and target shapes, we add the edge continuity constraint condition of the contour shape into the target matching energy cost function to improve the accuracy of shape matching. The experimental results of shape matching in a synthetic turbulence clutter scene and a real remote imaging scene show that compared with the traditional method, the proposed method can reduce the target matching error by about 6% in clutter scenes and reduce computational complexity.