Duplex Restricted Network With Guided Upsampling for the Semantic Segmentation of Remotely Sensed Images

Deep convolutional networks are of great significance for the automatic semantic annotation of remotely sensed images. Object position and semantic labeling are equally important in semantic segmentation tasks. However, the convolution and pooling operations of the convolutional network will affect the image resolution when extracting semantic information, which makes acquiring semantics and capturing positions contradictory. We design a duplex restricted network with guided upsampling. The detachable enhancement structure to separate opposing features on the same level. In this way, the network can adaptively choose how to trade-off classification and localization tasks. To optimize the detailed information obtained by encoding, a concentration-aware guided upsampling module is further introduced to replace the traditional upsampling operation for resolution restoration. We also add a content capture normalization module to enhance the features extracted in the encoding stage. Our approach uses fewer parameters and significantly outperforms previous results on two very high resolution (VHR) datasets: 84.81% (vs 82.42%) on the Potsdam dataset and 86.76% (vs 82.74%) on the Jiage dataset.