RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic
  Segmentation
Xianping Ma Xiaokang Zhang Man-On Pun
Semantic segmentation of remote sensing images is a fundamental task in
geoscience research. However, there are some significant shortcomings for the
widely used convolutional neural networks (CNNs) and Transformers. The former
is limited by its insufficient long-range modeling capabilities, while the
latter is hampered by its computational complexity. Recently, a novel visual
state space (VSS) model represented by Mamba has emerged, capable of modeling
long-range relationships with linear computability. In this work, we propose a
novel dual-branch network named remote sensing images semantic segmentation
Mamba (RS3Mamba) to incorporate this innovative technology into remote sensing
tasks. Specifically, RS3Mamba utilizes VSS blocks to construct an auxiliary
branch, providing additional global information to convolution-based main
branch. Moreover, considering the distinct characteristics of the two branches,
we introduce a collaborative completion module (CCM) to enhance and fuse
features from the dual-encoder. Experimental results on two widely used
datasets, ISPRS Vaihingen and LoveDA Urban, demonstrate the effectiveness and
potential of the proposed RS3Mamba. To the best of our knowledge, this is the
first vision Mamba specifically designed for remote sensing images semantic
segmentation. The source code will be made available at
https://github.com/sstary/SSRS.