Abstract
In this paper, we are dedicated to the cross-modal image retrieval between optical images and synthetic aperture radar (SAR) images. This cross-modal retrieval is a challenging task due to the different imaging mechanisms and huge heterogeneity gap. Here, we design a two-stream fully convolutional network to tackle this issue. The network maps the optical and SAR images to a common feature space for comparison. For different modal images, the comparable features are obtained by feeding them into the corresponding branch. Each branch fuses two types of features in a weighted manner. These two kinds of features root in the pooling features of VGG16 at different depths, but are refined by the well-designed channels-aggregated convolution (CAC) operation as well as semi-average pooling (SAP) operation. In order to get a better model, an extensible training approach is proposed. The training of the model is from the local to the whole. Besides, we collect an optical/SAR image retrieval (OSR) dataset. Comprehensive experiments on this dataset demonstrate the effectiveness of our proposed method.
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Acknowledgement
This work was supported in part by 973 Program under Contract 2015CB351803, by Natural Science Foundation of China (NSFC) under Contract 61390514 and 61331017, and by the Fundamental Research Funds for the Central Universities.
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Zhang, Y., Zhou, W., Li, H. (2018). Retrieval Across Optical and SAR Images with Deep Neural Network. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_36
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