Dense matching of aerial image based on deep siamese network considering color and texture features
Considering the problem of efficient image matching in image regions with repetitive or weak texture,the paper proposes an adaptive dense matching method for aerial stereo images based on deep Siamese network.A deep neural network model for stereo image dense matching is constructed using the concept of pixel-by-pixel translation matching within the framework of a siamese neural network.The feature vectors extracted from the left and right input images by two branch networks are used to obtain a matching cost volume and calculate the parallax value for each pixel.Subsequently,adaptive optimization of the parallax results is performed using color and texture features of ground objects to constrain mismatches in building edges,occlusions,and other areas,thereby enhancing prediction reliability and effectively avoiding mismatching.Finally,the proposed algorithm is validated using publicly available stereo image datasets.Experimental results demonstrate that our algorithm yields superior and smoother parallax maps compared to classical methods with over 30％improvement in matching accuracy without relying on GPU.
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