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Title:

Identifying {Corresponding} {Patches} in {SAR} and {Optical} {Images} with a {Pseudo}-{Siamese} {CNN}

Author(s):
Hughes, Lloyd H.; Schmitt, Michael; Mou, Lichao; Wang, Yuanyuan; Zhu, Xiao Xiang
Abstract:
In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two pat...     »
Keywords:
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Journal title:
IEEE Geoscience and Remote Sensing Letters
Year:
2018
Month:
jan
Fulltext / DOI:
doi:10.1109/LGRS.2018.2799232
Notes:
arXiv: 1801.08467
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