Integrating Coordinate Features in CNN-Based Remote Sensing Imagery Classification

The land cover classification has played an important role in remote sensing applications. However, most classification methods were designed based on the pixel features or local spatial features of the remote sensing image, which limits the classification accuracy and generalization. In order to further utilize the spatial information, this letter proposes a dual-branch neural network (NN) inspired by the conditional random field (CRF) model, namely CRF-Net, which takes into account the global spatial features of the image, i.e., geographic latitude-longitude information. First, a dual-branch NN is designed to extract the pixel features and coordinate features. Then, the two kinds of features are fused to realize the remote sensing imagery classification. In the experiments, randomly selected samples and spatial-disjoint samples are employed to verify the effectiveness of the proposed method for hyperspectral image (HSI) and polarimetric synthetic aperture radar (PolSAR) image classification. The experimental results show that the proposed method is superior to the traditional supervised classification methods under the spatial-disjoint sampling strategy, and can achieve the same level of accuracy under the random sampling condition.