Problem
Research questions and friction points this paper is trying to address.
State Space Models
Remote Sensing
Long-range Dependencies
Multi-modal Data
Temporal Data
State Space Models Meet Remote Sensing: A Survey
🤖 AI Summary
Remote sensing tasks confront significant challenges, including dense prediction, multimodal fusion, and long-sequence modeling, necessitating models capable of efficiently capturing long-range dependencies. This work presents the first systematic survey of state space models (SSMs) in remote sensing, offering a multidimensional analysis from the perspectives of task adaptation and architectural design. Leveraging the linear computational complexity inherent to SSMs, the study elucidates their advantages in handling multimodal and temporal remote sensing data, identifies key challenges, and outlines promising directions for future research. Additionally, the authors establish a continuously updated open-source repository to provide the community with a comprehensive reference and actionable insights.
📝 Abstract
State Space Models (SSMs), designed for long-range modeling, offer linear computational complexity and strong capabilities in capturing long-range dependencies. In the field of remote sensing, SSMs have gained popularity due to their effectiveness in addressing unique challenges such as dense visual predictions, multi-modal remote sensing data, and temporal remote sensing data, which have also yielded significant advancements in customized architectures. This paper presents a comprehensive review of SSM-based approaches in remote sensing, covering most of the relevant studies since SSMs were first introduced to the field. We offer a multi-dimensional analysis examining SSM applications in remote sensing tasks and discussing advancements in architecture design. This paper not only synthesizes the rapid progress in SSM-based research but also identifies key challenges and future opportunities. By providing a detailed perspective, this paper aims to serve as a foundational resource for remote sensing researchers, offering actionable insights to foster further advancements in this evolving domain. We will keep tracing related works at https://github.com/QinzheYang/Awesome-RS-State-Space-Model.
Innovation
Methods, ideas, or system contributions that make the work stand out.
State Space Models
Remote Sensing
Long-range Dependencies
Multi-modal Data
Temporal Modeling
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