Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model

TL;DR

Samba introduces a novel State Space Model-based framework for semantic segmentation of high-resolution remotely sensed images, outperforming CNN and ViT methods and establishing new benchmarks in the field.

Contribution

This paper is the first to apply State Space Models to semantic segmentation of remote sensing images, demonstrating superior performance over existing CNN and ViT approaches.

Findings

Samba achieves state-of-the-art results on LoveDA, ISPRS Vaihingen, and Potsdam datasets.

Samba outperforms traditional CNN and ViT methods in accuracy and efficiency.

The approach sets new performance benchmarks for remote sensing image segmentation.

Abstract

High-resolution remotely sensed images pose 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 in handling 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, ISPRS Vaihingen, and ISPRS Potsdam datasets, comparing its performance against top-performing CNN and ViT methods. The results reveal that Samba achieved unparalleled performance on commonly used remote sensing datasets for semantic segmentation. Our proposed Samba demonstrates for the first time the effectiveness of 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.