Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation

计算机科学 增采样 人工智能 分割 变压器 编码器 卷积神经网络 嵌入 模式识别(心理学) 计算机视觉 图像(数学) 量子力学 操作系统 物理 电压
作者
Xin He,Yong Zhou,Jiaqi Zhao,Di Zhang,Rui Yao,Yong Xue
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:60: 1-15 被引量:271
标识
DOI:10.1109/tgrs.2022.3144165
摘要

Global context information is essential for the semantic segmentation of remote sensing (RS) images. However, most existing methods rely on a convolutional neural network (CNN), which is challenging to directly obtain the global context due to the locality of the convolution operation. Inspired by the Swin transformer with powerful global modeling capabilities, we propose a novel semantic segmentation framework for RS images called ST-U-shaped network (UNet), which embeds the Swin transformer into the classical CNN-based UNet. ST-UNet constitutes a novel dual encoder structure of the Swin transformer and CNN in parallel. First, we propose a spatial interaction module (SIM), which encodes spatial information in the Swin transformer block by establishing pixel-level correlation to enhance the feature representation ability of occluded objects. Second, we construct a feature compression module (FCM) to reduce the loss of detailed information and condense more small-scale features in patch token downsampling of the Swin transformer, which improves the segmentation accuracy of small-scale ground objects. Finally, as a bridge between dual encoders, a relational aggregation module (RAM) is designed to integrate global dependencies from the Swin transformer into the features from CNN hierarchically. Our ST-UNet brings significant improvement on the ISPRS-Vaihingen and Potsdam datasets, respectively. The code will be available at https://github.com/XinnHe/ST-UNet .
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