FRF-Net: Land Cover Classification from Large-Scale VHR Optical Remote Sensing Images

Deep learning (DL) technique is widely applied in remote sensing (RS) applications because of its outstanding nonlinear feature extraction ability. However, with regard to the issues of large-scale and very high-resolution (VHR) land cover classification, multi-object distributions and clear appearance with large intraclass difference become challenges for refined pixelwise land cover mapping. Focusing on these problems, the letter proposed a novel encoding-to-decoding method called the full receptive field (RF) network (FRF-Net) based on two types of attention mechanism. In the FRF-Net, ResNet-101 is used as the basic backbone. Then, the ensemble feature is generated by encoding the high-level features based on the self-attention mechanism which could achieve full RF to capture long-range semantic. Next, the encoding result is decoded by the fusion attention mechanism combined with the low-level feature to produce a fusion feature which contains a refined semantic description for accurate land cover mapping. Extensive experiments based on the GID and ISPRS data sets proved that the proposed network outperforms the state-of-the-art methods. The FRF-Net achieved 66.71% and 64.17% of the mean of classwise Intersection over Union (mIOU) with smaller computation cost on ISPRS and GID, respectively.