SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
This paper presents a novel framework for enhancing semantic segmentation in remote sensing imagery by leveraging the Segment Anything Model (SAM). The paper addresses the unique challenges posed by the intrinsic differences between natural images and remote sensing images, emphasizing enhancements through SAM-Generated Object (SGO) and SAM-Generated Boundary (SGB) outputs. The research articulates two innovative loss functions—object consistency loss and boundary preservation loss—that assist in refining segmentation performance by utilizing SAM's detailed raw outputs without the need for additional complex fine-tuning mechanisms or prompts.
Framework Overview and Methodology
The proposed framework seeks to overcome two primary limitations of SAM in remote sensing applications: the absence of semantic labels and the fragmentation and inaccuracy of boundaries in current segmentation maps. To address these challenges, the researchers introduce a comprehensive processing phase that exploits SAM’s zero-shot segmentation capabilities to generate SGOs and SGBs. These outputs form the basis for two newly introduced loss functions:
- Object Consistency Loss: This function focuses on preserving consistency within segmented objects. By enforcing uniformity within the pixels of an object, the method enhances semantic segmentation outcomes in datasets where models typically face difficulties due to object complexity and lack of semantic information.
- Boundary Preservation Loss: This function capitalizes on the boundary details inherent in SGB, directing the semantic segmentation model to focus on edge information. Such emphasis is crucial for accurately delineating objects in high-resolution remote sensing imagery where boundary precision is paramount.
Experimental Validation
The framework was tested on two renowned datasets—ISPRS Vaihingen and LoveDA Urban—and benchmarked against four prominent semantic segmentation models: ABCNet, CMTFNet, UNetformer, and FTUNetformer. Performance improvements were notable across these models, confirming the framework's applicability and versatility. Specifically, enhancements in mF1 and mIoU metrics were documented, illustrating the framework's ability to effectively harness the power of SAM in diverse contextual settings of remote sensing.
Key Findings and Implications
The results demonstrated remarkable improvements in classes with regular shapes and distinct boundaries, such as buildings and vehicles. However, the performance in categories with intricate boundaries, like vegetation, also benefited due to the enriched boundary information provided by SGB. The integration of SAM in semantic segmentation tasks without necessitating additional task-specific modules marks a significant step towards more straightforward and effective model implementations in remote sensing.
The proposed framework effectively bridges the gap between general-purpose segmentation models and remote sensing-specific semantic segmentation needs. It opens new avenues for applying SAM in domains requiring precise segmentation, potentially extending to more complex multi-class segmentation tasks and other remote sensing applications. Future research may further explore enhancing SAM's capabilities with less dependency on explicit prompt designs and fine-tuning techniques under varied imaging conditions.
Conclusion
This paper presents a well-structured and effective approach for improving semantic segmentation in remote sensing imagery using SAM. The introduction of object and boundary constraints through the proposed loss functions elevates the segmentation performance of traditional models, highlighting the latent potential of SAM's outputs. The research provides a foundation for future exploration of large models like SAM in remote sensing applications, offering insights that are relevant for both academic research and practical implementations in geospatial analysis.