多尺度PCA-HOG遥感异源图像匹配算法

针对遥感异源图像匹配中非线性灰度畸变和强噪声干扰问题,提出一种基于主成分分析(Principal Components Analysis, PCA)和方向梯度直方图(Histogram of Oriented Gradients, HOG)的遥感异源图像匹配算法。该算法利用HOG提取图像间的几何结构共性特征,能有效克服异源图像非线性灰度畸变的问题;提出一种快速多尺度PCA算法,能对HOG中的局部梯度方向进行增强,从而能在强噪声干扰的情况下,准确提取出图像的结构特征。为了提高算法的计算速度,利用积分图像降低特征提取过程的计算复杂度,并利用快速傅里叶变换实现高效率的匹配搜索。实验利用多种遥感异源图像(包括可见光图像、合成孔径雷达图像和红外图像)对提出的匹配算法进行了验证。结果表明,与现有算法相比,该算法在匹配性能上有明显提升。

Remote sensing multi-modal image matching algorithm based onmulti-scale PCA-HOG

To solve the problem of non-linear gray level distortion and strong noise interference in remote sensing multi-modal image matching, a remote sensing multi-modal image matching algorithm based on PCA (principal components analysis) and HOG (histogram of oriented gradients) was proposed. This algorithm uses HOG to extract the common features of geometric structure between images, which can effectively overcome the problem of nonlinear grayscale distortion of multi-modal images. Besides, a fast multi-scale PCA algorithm was proposed to enhance the local gradient direction in HOG, so that it can accurately extract the structural features of the image under the condition of strong noise interference. In order to improve the calculation speed of the algorithm, the integrated image method was used to reduce the computational complexity of the feature extraction process, and the fast Fourier transform was used to achieve a highly efficient matching search. The experiment used a variety of remote sensing multi-modal images (including visible light images, synthetic aperture radar images, and infrared images) to verify the matching algorithm. The results show that, compared with existing algorithms, the proposed algorithm significantly improves the matching performance.