Feature matching, which refers to finding the correct correspondences from two sets of features, is an important step in feature-based image registration. In this letter, an accurate and highly robust point-matching algorithm, which is called the integrated spatial structure constraint, is proposed. We establish a set of tentative correspondences using the scale-invariant feature transform algorithm and then focus on increasing the number of correct correspondences (inliers) and removing incorrect correspondences (outliers). First, a global structure constraint, i.e., the shape context, is constructed for each correspondence out of the tentative set to increase the number of inliers and raise the correct rate simultaneously. Then, a local structure constraint based on the triangle area representation is utilized on the neighboring points of each correspondence to remove outliers. Experimental results compared with four state-of-the-art methods demonstrate that the proposed algorithm is robust and can achieve preferable results in terms of both matching accuracy and quantity of inliers.