针对SIFT（scale invariant feature transform）特征描述符因仅利用特征点的局部邻域信息而对散落在图像内相似结构中的点极易发生误匹配的现象，提出了一种基于空间分布描述符的SIFT误匹配校正方法。该方法首先利用SIFT算法进行匹配；然后对于匹配结果中的特征点，再利用图像轮廓像素点对该点的空间分布信息进行重新描述，以形成一种独特性更高的空间分布描述符；最后运用此种描述符，对匹配结果中存在的“一对多”和“一对一”的错误匹配形式，分别采取两种不同的匹配策略进行校正。以真实图像进行的实验结果表明，该方法与RANSAC（随机抽样一致性)算法相比，其在不损失正确匹配的前提下，能够真正提高正确匹配率。

SIFT(scale invariant feature transform) descriptor usually leads to mismatching because it uses the gradient information in the neighborhood of one feature point, when the extracted feature points locate in some similar structures of one image. So a method to correct SIFT mismatching based on a kind of spatial distribution descriptor is proposed. Because the spatial distribution of pixels on the image contour are different aiming at different matching points, each matching point obtained by SIFT can be described again to generate a more distinctive descriptor. Then the method corrects two kinds of mismatching using corresponding correction strategies by the new descriptor. Through the experiments on the real images, the comparing results between the algorithm and RANSAC indicate that the correction method can improve the percentage of correct matching under the condition of remaining the original right matching.