A performing analysis of unsupervised dense matching feature extraction networks
JIN Fei, GUAN Kai, LIU Zhi, HAN Jiarong, RUI Jie, LI Qinggao
2022, 51(3):
426-436.
doi:10.11947/j.AGCS.2022.20200503
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With the development of artificial intelligence, supervised dense matching method based on deep learning has achieved good performance in virtual, indoor and driving data sets. In view of the difficulty in obtaining aerial image dense matching tag data, we use unsupervised dense matching framework for reference, and test the matching accuracy on aerial image data set and referential close range data set respectively, and realize the qualitative analysis of the relationship between network structure module and precision, which provides a further exploration of the practical application of deep learning in the field of surveying and mapping, and has important reference value. Under the same loss function condition, DispNetS, DispNetC, iResNet, GCNet, PSMNetB and PSMNetS network structures are used to test. Through analysis, the following conclusions are obtained:① Among the tested network structures, PSMNetS has the highest accuracy in aerial image data set and close range data set, and has the potential of practical application; ② The network with better performance in the supervised method may not have better performance in the unsupervised method. Its accuracy depends not only on the matching ability of the network itself, but also on the compatibility between the network and the loss function; ③ The twin network module, related information fusion module, pyramid pooling module and stacked hourglass module have good compatibility with unsupervised loss function, which can improve the network accuracy, while iResNet's image reconstruction iterative refinement module and reconstruction loss function repeat constraints, which will produce a "negative optimization" effect.
