Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model
CoRR(2024)
摘要
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
https://github.com/zhuqinfeng1999/Samba.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要