# **New Intelligence Report**
**Editor:** LRST
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## **Summary Overview**
Powered by **big data** and **large models**, fine-tuning has emerged as a powerful, low-cost, and efficient method for tackling **complex remote sensing scenarios** — especially those involving **small samples** and **long-tail targets**.
### **Key Evolution Path**
1. **Full-Parameter Fine-Tuning** – Early approach, updating most model parameters to achieve task transfer.
2. **Parameter-Efficient Fine-Tuning (PEFT)** – Techniques like adapters, prompts, and re-parameterization.
3. **Hybrid Fine-Tuning** – Unified frameworks integrating multiple PEFT methods for better scalability.
A joint team led by **Tsinghua University** published a review in **CVMJ** outlining nine research directions to strengthen remote sensing applications in **agricultural monitoring**, **weather forecasting**, and beyond.
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## **Foundation Models + Fine-Tuning: The New Paradigm**
In recent years, the focus in remote sensing image interpretation has shifted from designing model architectures to the paradigm of **"foundation models + fine-tuning"**, which enables:
- **Better transferability**
- **Improved application performance**
- **Lower resource costs**
> **Challenges addressed:** small datasets, long-tail targets, limited computing resources.
**Figure 1** – Role of foundation models and fine-tuning in downstream remote sensing task adaptation.
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## **Technology Evolution Timeline**
- **Early Stage:** Full-parameter fine-tuning for cross-task transfer.
- **PEFT Stage:** Low-cost adaptation methods like Adapters, Prompt Tuning, and LoRA.
- **Hybrid Stage:** Combining multiple PEFT techniques for **multi-modality** and **multi-task** adaptability.
**Figure 2** – Timeline of representative remote sensing fine-tuning technologies.
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## **Research Review by Leading Institutions**
Researchers from **Tsinghua**, **Nankai**, **Hunan**, **Wuhan University**, and **CAS** trace the progression from traditional fine-tuning to modern hybrid PEFT paradigms.
**Paper:** [https://ieeexplore.ieee.org/document/11119145](https://ieeexplore.ieee.org/document/11119145)
**Code:** [https://github.com/DongshuoYin/Remote-Sensing-Tuning-A-Survey](https://github.com/DongshuoYin/Remote-Sensing-Tuning-A-Survey)
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## **Six Core Fine-Tuning Paradigms**
**Figure 3** – Representative paradigms of parameter-efficient fine-tuning.
1. **Adapter Tuning** – Add lightweight trainable modules into frozen models.
2. **Prompt Tuning** – Learn prompt vectors to guide frozen models.
3. **Reparameterized Tuning (e.g., LoRA)** – Low-rank decomposition to minimize trainable parameters.
4. **Hybrid Tuning** – Combine two or more approaches to enhance flexibility.
5. **Partial Tuning** – Fine-tune only selected model layers.
6. **Improved Tuning** – Optimize full-parameter fine-tuning with new strategies and losses.
**Figure 4** – Overview of remote sensing fine-tuning techniques.
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## **Representative Applications in Remote Sensing**
- **Adapter Tuning**
- **AiRs**: Spatial Context Adapters (SCA) & Semantic Response Adapters (SRA).
- **SCD-SAM**: Improved overlapping patch handling and multi-scale semantic integration.
- **Prompt Tuning**
- **RSPrompter**: Chain-of-Thought prompting for multi-step reasoning in context-rich images.
- **Reparameterized Tuning**
- **LoRA-NIR**: Optimized for near-infrared imagery.
- **LoRA-SAM**: Applied to segment roads and water bodies.
- **Hybrid Tuning**
- **Upetu**: Multi-technique integration.
- **MSF-SAM**: Combines Adapter + LoRA.
- **Improved Tuning Strategies**: Metric discriminative loss + knowledge distillation to reduce catastrophic forgetting.
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## **Datasets for Fine-Tuning**
**Table 1** – Summary of datasets for remote sensing fine-tuning.
Covers:
- **Modalities:** Optical, SAR, Hyperspectral, Point Cloud, Text-Image multimodal.
- **Tasks:** Dehazing, Change Detection, Segmentation, Detection, Captioning.
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## **Challenges & Future Research Directions**
**Primary Focus Areas**
- **Efficient few-shot fine-tuning** for rare targets.
- **New application domains:** super-resolution, dehazing, object tracking.
- **Optimizing RS Foundation Models (RSFM)** for performance.
- **Leveraging RS-specific characteristics** in custom fine-tuning designs.
**Forward-Looking Strategies**
- Introduce **new PEFT paradigms** (structured sparsity, quantization-aware tuning).
- Explore **multi-approach hybrid tuning** combinations (Adapter + LoRA + Prompt).
- Develop **fine-tuning theory** for RS.
- **Optimize training configurations** (learning rate, layer count, optimizer choice).
- Research **scaling laws** linking model size, data volume, and performance.
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## **Conclusion**
**Key Takeaway:** The combination of **foundation models + fine-tuning** is setting new standards in remote sensing efficiency. A thorough understanding of evolution from full-parameter to hybrid fine-tuning helps navigate future innovations.
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**Reference:**
[https://ieeexplore.ieee.org/document/11119145](https://ieeexplore.ieee.org/document/11119145)
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