Abstract
Polarization images can highlight the reflection attribute of the target and enhance the display effect of the target boundary on the images. More comprehensive target attributes can be obtained from images taken from multiple polarization angles. However, the number of polarization images is limited at present, and it is difficult to carry out effective research on the premise of small samples. Therefore, based on the polarization images, we give full play to the advantages of polarization images, improve the traditional neural network, and introduce the visual attention model to focus on the salient region. Based on the structural similarity between hyperspectral images and polarization images, twin convolution neural network is constructed to carry out the research of transfer learning in order to learn the network parameters in the case of small samples, and finally realize the target extraction.
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Zhou, J., Fei, F., Wang, Z., Nie, C. (2022). A Target Extraction Algorithm Based on Polarization Image Attention Mechanism. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1586. Springer, Cham. https://doi.org/10.1007/978-3-031-06767-9_1
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