基于深度学习的异源立体影像对匹配方法

相对于同源影像立体匹配，基于无人机倾斜摄影与近景摄影获取的异源影像在空间特征、视场角及分辨率等方面均存在较大的差异，给影像匹配带来困难。本文利用基于单应性变换的卷积神经网络提取特征点，在匹配阶段采用交叉注意力机制的图神经网络进行特征点匹配。该方法较好地克服了异源影像间因存在较大视差和扭曲变换而导致的匹配效果较差的问题，并以河北省廊坊市大城县的马家祠堂为试验数据，对比传统SURF （加速稳定性征）算法与深度学习算法的匹配效果。结果表明，基于深度学习算法对存在大视角差异的异源影像的匹配率更高。

Matching method of heterologous stereo image pairs based on deep learning

Compared with stereo matching of homologous images, there are great differences in spatial features, field angle and resolution between the heterogenous images acquired by UAV tilt photography and close-range photography, which bring difficulties to image matching. In this paper, the feature points are extracted by using the convolutional neural network based on homography transformation, and the graph neural network with cross-attention mechanism is used to match the feature points in the matching stage. It overcomes the problem of poor matching effect caused by large disparity and distorted transformation between different images. In this paper, Ma jiacitang in Dacheng county, Langfang city, Hebei province is taken as the experimental data, and the matching effect of traditional SURF (accelerated robust feature) algorithm and deep learning algorithm is compared. The results show that the algorithm based on deep learning has a higher matching rate for heterologous images with large perspective differences.