A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images

TL;DR

This paper introduces a new transformer-based semantic segmentation method for high-resolution remote sensing images, utilizing Swin Transformer and a novel decoder to improve context extraction and segmentation accuracy.

Contribution

It proposes a transformer-based backbone and a densely connected feature aggregation decoder, advancing beyond traditional FCN architectures for remote sensing image segmentation.

Findings

Outperforms existing methods on two remote sensing datasets

Effectively captures context with Swin Transformer backbone

Improves segmentation accuracy and resolution restoration

Abstract

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