High-resolution remotely sensed images poses a challenge for commonly used
semantic segmentation methods such as Convolutional Neural Network (CNN) and
Vision Transformer (ViT). CNN-based methods struggle with handling such
high-resolution images due to their limited receptive field, while ViT faces
challenges to handle long sequences. Inspired by Mamba, which adopts a State
Space Model (SSM) to efficiently capture global semantic information, we
propose a semantic segmentation framework for high-resolution remotely sensed
images, named Samba. Samba utilizes an encoder-decoder architecture, with Samba
blocks serving as the encoder for efficient multi-level semantic information
extraction, and UperNet functioning as the decoder. We evaluate Samba on the
LoveDA dataset, comparing its performance against top-performing CNN and ViT
methods. The results reveal that Samba achieved unparalleled performance on
LoveDA. This represents that the proposed Samba is an effective application of
the SSM in semantic segmentation of remotely sensed images, setting a new
benchmark in performance for Mamba-based techniques in this specific
application. The source code and baseline implementations are available at
this https URL

该研究提出了一种针对高分辨率遥感图像的语义分割框架，名为 Samba，其采用编码器 - 解码器架构，使用 Samba 块作为编码器来高效地提取多层语义信息，以及 UperNet 作为解码器。在 LoveDA 数据集上的评估结果表明，Samba 在 LoveDA 上取得了前所未有的性能，为 Mamba 技术在这一特定应用中的性能设定了新的基准。