  The fully convolutional network (FCN) with an encoder-decoder architecture
has been the standard paradigm for semantic segmentation. The encoder-decoder
architecture utilizes an encoder to capture multilevel feature maps, which are
incorporated into the final prediction by a decoder. As the context is crucial
for precise segmentation, tremendous effort has been made to extract such
information in an intelligent fashion, including employing dilated/atrous
convolutions or inserting attention modules. However, these endeavors are all
based on the FCN architecture with ResNet or other backbones, which cannot
fully exploit the context from the theoretical concept. By contrast, we
introduce the Swin Transformer as the backbone to extract the context
information and design a novel decoder of densely connected feature aggregation
module (DCFAM) to restore the resolution and produce the segmentation map. The
experimental results on two remotely sensed semantic segmentation datasets
demonstrate the effectiveness of the proposed scheme.Code is available at
https://github.com/WangLibo1995/GeoSeg
