  The land cover classification has played an important role in remote sensing
because it can intelligently identify things in one huge remote sensing image
to reduce the work of humans. However, a lot of classification methods are
designed based on the pixel feature or limited spatial feature of the remote
sensing image, which limits the classification accuracy and universality of
their methods. This paper proposed a novel method to take into the information
of remote sensing image, i.e., geographic latitude-longitude information. In
addition, a dual-branch convolutional neural network (CNN) classification
method is designed in combination with the global information to mine the pixel
features of the image. Then, the features of the two neural networks are fused
with another fully neural network to realize the classification of remote
sensing images. Finally, two remote sensing images are used to verify the
effectiveness of our method, including hyperspectral imaging (HSI) and
polarimetric synthetic aperture radar (PolSAR) imagery. The result of the
proposed method is superior to the traditional single-channel convolutional
neural network.
