Pan-Mamba: Effective Pan-Sharpening with State Space Model

Khái niệm cốt lõi

State-of-the-art pan-sharpening technique, Pan-Mamba, leverages the Mamba model for efficient global feature extraction and cross-modal information fusion.

Tóm tắt

The content introduces Pan-Mamba, a novel pan-sharpening network that utilizes the efficiency of the Mamba model in global information modeling. It discusses the challenges in traditional pan-sharpening methods and highlights the advancements made by deep learning-based models. The paper details the architecture of Pan-Mamba, including Mamba blocks, channel swapping Mamba blocks, and cross-modal Mamba blocks. Extensive experiments across diverse datasets demonstrate superior fusion results compared to state-of-the-art methods.

Introduction:

• High-resolution multi-spectral image acquisition challenges.
• Importance of pan-sharpening techniques.

Recent Advancements:

• Evolution from manual rules to deep learning methods.
• Limitations of current deep learning-based models.

Proposed Approach - Pan-Mamba:

• Leveraging Mamba model for global information modeling.
• Customized components for efficient cross-modal fusion.

Experimental Results:

• Surpassing state-of-the-art methods in fusion results.
• First attempt at integrating Mamba model into pan-sharpening domain.

Thống kê

"Our proposed approach surpasses state-of-the-art methods."
"This work is the first attempt in exploring the potential of the Mamba model."

Trích dẫn

"Our contribution, Pan-Mamba, represents a novel pan-sharpening network that leverages the efficiency of the Mamba model in global information modeling."
"To the best of our knowledge, this work is the first attempt in exploring the potential of the Mamba model."

Thông tin chi tiết chính được chắt lọc từ

Pan-Mamba

by Xuanhua He,K... lúc arxiv.org 03-12-2024

https://arxiv.org/pdf/2402.12192.pdf

Yêu cầu sâu hơn

How can Pan-Mamba's efficiency impact real-world applications beyond remote sensing

Pan-Mamba's efficiency can have a significant impact on real-world applications beyond remote sensing by enhancing image processing tasks in various domains. The efficient feature extraction and fusion capabilities of Pan-Mamba can be leveraged in fields like medical imaging for improving diagnostic accuracy, analyzing complex datasets, and enhancing visualization of medical scans. In satellite technology, the efficiency of Pan-Mamba can lead to faster processing times for high-resolution multi-spectral images, enabling quicker data analysis and decision-making processes. Additionally, industries such as agriculture, urban planning, environmental monitoring, and disaster management could benefit from the enhanced image fusion techniques offered by Pan-Mamba.

What counterarguments exist against using deep learning-based models like Pan-Mamba for image processing

Counterarguments against using deep learning-based models like Pan-Mamba for image processing may include concerns about model complexity leading to overfitting or lack of interpretability. Deep learning models often require large amounts of data for training and tuning parameters effectively which might not always be available in practical scenarios. Additionally, there could be challenges related to computational resources required for running deep learning algorithms efficiently. Interpretability issues arise due to the black-box nature of deep learning models making it difficult to understand how decisions are made within the model.

How might advancements in image fusion techniques like Pan-Mamba influence other fields such as medical imaging or satellite technology

Advancements in image fusion techniques like Pan-Mamba have the potential to revolutionize other fields such as medical imaging by improving the quality of fused images obtained from different modalities (e.g., MRI and CT scans). This can enhance diagnostic accuracy and aid in treatment planning processes. In satellite technology, advancements in image fusion techniques can lead to better resolution images that provide more detailed insights into Earth's surface features aiding in environmental monitoring or disaster response efforts. Furthermore, these advancements could also find applications in robotics vision systems where precise visual information is crucial for navigation and object recognition tasks.