TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation
This work addresses a limitation in segmentation networks for remote sensing by improving global-local feature interactions, though it is incremental as it builds on existing UNet-based architectures.
The paper tackles the problem of global contextual dependencies across multiple resolutions in remote sensing image semantic segmentation by introducing TNet, a Terrace Convolutional Decoder Network that uses convolution and addition to integrate low-resolution features into higher-resolution ones, achieving mIoU scores of 85.35% on ISPRS Vaihingen, 87.05% on ISPRS Potsdam, and 52.19% on LoveDA.
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.