TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation

• URL: http://arxiv.org/abs/2508.04061v1
• Date: Wed, 06 Aug 2025 03:44:30 GMT
• Title: TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation
• Authors: Chengqian Dai, Yonghong Guo, Hongzhao Xiang, Yigui Luo,
• Abstract summary: In remote sensing, most segmentation networks adopt the UNet architecture, often incorporating modules such as Transformers.<n>We introduce the Terrace Convolutional Decoder Network (TNet), a simple yet effective architecture that leverages only convolution and addition operations.<n>We implement TNet with a ResNet-18 encoder (TNet-R) and evaluate it on three benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In remote sensing, most segmentation networks adopt the UNet architecture, often incorporating modules such as Transformers or Mamba to enhance global-local feature interactions within decoder stages. However, these enhancements typically focus on intra-scale relationships and neglect the global contextual dependencies across multiple resolutions. To address this limitation, we introduce the Terrace Convolutional Decoder Network (TNet), a simple yet effective architecture that leverages only convolution and addition operations to progressively integrate low-resolution features (rich in global context) into higher-resolution features (rich in local details) across decoding stages. This progressive fusion enables the model to learn spatially-aware convolutional kernels that naturally blend global and local information in a stage-wise manner. We implement TNet with a ResNet-18 encoder (TNet-R) and evaluate it on three benchmark datasets. TNet-R achieves competitive performance with a mean Intersection-over-Union (mIoU) of 85.35\% on ISPRS Vaihingen, 87.05\% on ISPRS Potsdam, and 52.19\% on LoveDA, while maintaining high computational efficiency. Code is publicly available.

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