The global information for land cover classification by dual-branch deep learning
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Land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image so as to reduce the work of human. 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-channel convolutional neural network (CNN) classification method is designed to mine pixel feature of image in combination with the global information simultaneously. Firstly, 1-demensional network of CNN is designed to extract pixel information of remote sensing image, and the fully connected network (FCN) is employed to extract latitude-longitude feature. Then, their features of two neural networks are fused by another fully neural network to realize remote sensing image classification. Finally, two kinds of remote sensing, involving hyperspectral imaging (HSI) and polarimetric synthetic aperture radar (PolSAR), are used to verify the effectiveness of our method. The results of the proposed method is superior to the traditional single-channel convolutional neural network.
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