Identifying corresponding patches in SAR and optical imagery with a convolutional neural network
Lichao Mou, Michael Schmitt, Yuanyuan Wang, Xiao Xiang Zhu · 2017
In this paper, we investigate making use of a convolutional neural network (CNN) to solve the task of identifying corresponding patches in very high resolution (VHR) optical and SAR imagery of complicated urban scenery. By doing so, the binary decision function is learnt directly from automatically generated training data and does not resort to any hand-crafted features. First evaluations show great potential for further studies towards a generalized multi-sensor matching procedure.