异源图像由于亮度和对比度差异较大，采用基于灰度和梯度信息的局部特征匹配方法匹配正确率较低。针对该问题，提出一种基于相位一致性和梯度方向直方图的异源图像匹配方法。该方法首先采用具有亮度和对比度不变性的相位一致性方法提取异源图像特征点和边缘图像，并以特征点为中心，选取100×100的边缘图像作为特征区域，统计梯度方向直方图，生成64维特征描述符；然后，选用归一化相关函数作为匹配测度，采用双点匹配方法选取一个特征点的两个较优的候选匹配点，并采用RANSAC方法进行匹配点提纯；最后，基于局部归一化互信息方法和最优化方法进行匹配点精确定位，提高匹配精度。实验结果表明，该方法在可见光、近红外、中波红外和长波红外等异源图像匹配中具有较好的匹配性能，平均匹配正确率高达88％，是SURF匹配方法的3.4倍。%The common local feature matching methods based on gradient histogram are difficult to match correctly due to the difference of contrast and luminance of heterogonous image. For solving this problem,the heterogonous image matching method was proposed based on the phase congruency and histograms of oriented gradients. Firstly,feature points and edge image of heterogonous images were extracted by using phase congruency method which has invariance of luminance and contrast,and then the 64-dimensional feature descriptor was generated by counting histograms of ori-ented gradients of squared feature area whose size is 1 00 ×1 00. Secondly,for matching heterogonous image pair,nor-malized correlation function is selected as similarity measure,and two better candidate matching point pairs of one fea-ture point was first selected by using dual-point matching method,then matching point pair was purified by using RANSAC method. Finally,the location of matching point was refined using optimization method and local normalized mutual information. The experimental results indicate that the proposed method can achieve higher performance in heterogonous image matching,the average matching correct rate is up to 88% and is 3.4 times of SURF matching method.
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