Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
Muyi Bao, Shuchang Lyu, Zhaoyang Xu, Huiyu Zhou, Jinchang Ren, Shiming, Xiang, Xiangtai Li, Guangliang Cheng

TL;DR
This paper surveys the emerging Mamba architecture in remote sensing, highlighting its advantages over traditional CNNs and ViTs, and systematically analyzing its applications, innovations, and future challenges.
Contribution
It is the first comprehensive review of Mamba architectures in remote sensing, providing a taxonomy, benchmarking results, and future research directions.
Findings
Mamba architectures outperform traditional methods in several remote sensing tasks.
Micro- and macro-architectural innovations enhance Mamba's effectiveness.
Benchmarking shows Mamba's superior scalability and accuracy.
Abstract
Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of vision Mamba…
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Taxonomy
TopicsRemote Sensing and Land Use · Satellite Image Processing and Photogrammetry · Remote-Sensing Image Classification
MethodsMamba: Linear-Time Sequence Modeling with Selective State Spaces
