TNet：面向遥感图像语义分割的阶地卷积解码器网络
TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation
AI总结：
针对现有遥感分割网络解码器忽略多分辨率全局上下文依赖的问题，提出仅用卷积与加法实现跨分辨率特征渐进融合的TNet，搭配ResNet-18编码器在三个基准数据集上取得了兼具高精度与高效率的分割效果。
摘要：
在遥感领域，大多数分割网络采用UNet架构，通常会在解码器阶段引入Transformer或Mamba等模块来增强全局-局部特征交互。然而，这些增强方法通常聚焦于尺度内的关联，却忽略了多分辨率之间的全局上下文依赖关系。为解决这一局限，我们提出了阶地卷积解码器网络（Terrace Convolutional Decoder Network，TNet），这是一种简洁却有效的架构，仅利用卷积和加法运算，在解码各阶段将富含全局上下文的低分辨率特征逐步融合到富含局部细节的高分辨率特征中。这种渐进式融合使模型能够学习具备空间感知能力的卷积核，以分阶段的方式自然融合全局与局部信息。我们采用ResNet-18编码器实现TNet（记为TNet-R），并在三个基准数据集上进行评估。TNet-R取得了具有竞争力的性能：在ISPRS Vaihingen数据集上的平均交并比（mIoU）为85.35%，在ISPRS Potsdam数据集上为87.05%，在LoveDA数据集上为52.19%，同时保持了较高的计算效率。代码已公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
