数字农科院2.0

TMNet: A Two-Branch Multi-Scale Semantic Segmentation Network for Remote Sensing Images

文献类型： 外文期刊

作者： Yupeng Gao;Shengwei Zhang;Dongshi Zuo;Weihong Yan;Xin Pan

作者机构：

关键词： global modeling;remote sensing images;semantic segmentation;Swin transformer

期刊名称： Sensors (Basel, Switzerland)

ISSN： 1424-8220

年卷期： 2023 年 23 卷 13 期

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Pixel-level information of remote sensing images is of great value in many fields. CNN has a strong ability to extract image backbone features, but due to the localization of convolution operation, it is challenging to directly obtain global feature information and contextual semantic interaction, which makes it difficult for a pure CNN model to obtain higher precision results in semantic segmentation of remote sensing images. Inspired by the Swin Transformer with global feature coding capability, we design a two-branch multi-scale semantic segmentation network (TMNet) for remote sensing images. The network adopts the structure of a double encoder and a decoder. The Swin Transformer is used to increase the ability to extract global feature information. A multi-scale feature fusion module (MFM) is designed to merge shallow spatial features from images of different scales into deep features. In addition, the feature enhancement module (FEM) and channel enhancement module (CEM) are proposed and added to the dual encoder to enhance the feature extraction. Experiments were conducted on the WHDLD and Potsdam datasets to verify the excellent performance of TMNet.

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