Abstract: Due to the different imaging mechanism of visible and SAR images， the content of images is different， the depth features are difficult to align， and the correlation speed is slow. A depth cross modal hash network model is proposed to realize the cross modal correlation between SAR images and optical images. Firstly， aiming at the great difference of color information between SAR and optical remote sensing images， an image transformation mechanism is proposed. Four different types of spectral images are generated from optical image conversion and input into the network， which disrupts the color channel， so that the network pays more attention to the texture and contour information of the image， but is not sensitive to the color information； secondly， aiming at the high noise of SAR image， two modal images in the same scene are generated. For example， the content is heterogeneous， image pair training strategy is proposed to reduce the feature difference between cross-modal images； then， aiming at the low correlation efficiency and high storage consumption， a triple hash loss function is proposed to improve the association accuracy of the model and reduce the association time. Finally， a SAR and optical dual-mode remote sensing data set is constructed. The experimental part verifies the practicability of the data set and the effectiveness of the proposed algorithm.

Key words: multi-source heterogeneous, remote sensing image, association learning, deep neural network, visible image