DO-Net: Dual-Output Network for Land Cover Classification From Optical Remote Sensing Images

Land cover classification is the basic task of remote sensing image interpretation. Related methods have developed rapidly, especially the branch based on deep learning (DL). For high-resolution remote sensing images, the smaller inter-class difference and greater intra-class difference are two obstacles to improving the classification accuracy. For the former, the DL models generally use a deeper encoder to extract more powerful classification features. Considering that the scale of different land cover categories varies greatly, multi-scale feature extraction modules are also used to improve the classification accuracy. While the latter is always overlooked, and thus we propose a dual-output model, which uses a dense spatial pyramid pooling (DSPP) module to generate both the pixel-level and region-level predictions, to reduce the influence of intra-class differences. To further increase the classification accuracy, we investigate the band selection technique to apply the pre-trained encoder from the natural red green blue (RGB) dataset to multi-spectral remote sensing images. Extensive experiments on two datasets demonstrate the effectiveness of our model.