arXiv:2503.07911v1 Announce Type: new
Abstract: Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing more complex. Secondly, the CLIP model, mainly designed for global feature alignment in foundational models, often overlooks local objects crucial to remote sensing. This oversight leads to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Thirdly, both models have not been pre-trained on multi-scale aerial views, increasing the likelihood of detection failures. To tackle these challenges, we introduce the innovative VTPSeg pipeline, utilizing the strengths of Grounding DINO, CLIP, and SAM for enhanced open-vocabulary image segmentation. The Grounding DINO+(GD+) module generates initial candidate bounding boxes, while the CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes, ensuring that only pertinent objects are considered. Subsequently, these refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. Our VTPSeg is validated by experimental and ablation study results on five popular remote sensing image segmentation datasets.
Pixal-Level Segmentation in Remote Sensing: Enhanced Open-Vocabulary Image Segmentation
The field of remote sensing holds great potential for various applications, such as environmental monitoring, urban planning, and infrastructure management. However, extracting accurate and detailed information from remote sensing imagery remains a challenge. Pixal-level segmentation, which involves classifying each pixel in an image into specific objects or classes, is a crucial task in remote sensing.
In recent years, vision models like CLIP (Contrastive Language–Image Pretraining) and Segment Anything Model (SAM) have shown promising results in zero-shot segmentation tasks. These models leverage large-scale pretraining on diverse visual and textual data to learn powerful representations. However, when it comes to remote sensing, specific challenges need to be addressed to improve the accuracy and efficiency of segmentation.
Challenge 1: Redundant masks and post-processing complexity
The SAM model, although effective, often generates redundant masks due to the lack of clear prompt constraints. This means that multiple masks may be produced for a single object, making post-processing more complex. Finding a way to generate concise and accurate masks is vital for efficient segmentation in remote sensing imagery.
Challenge 2: Overlooking local objects
The CLIP model, originally designed for global feature alignment in foundational models, tends to overlook local objects that are crucial in remote sensing. This oversight can lead to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Addressing this issue is necessary to ensure that all relevant objects are properly identified and segmented.
Challenge 3: Lack of pre-training on multi-scale aerial views
Both the CLIP and SAM models have not been pretrained on multi-scale aerial views, which are common in remote sensing. This limitation increases the likelihood of detection failures, as the models may struggle to accurately segment objects at different scales. Incorporating pre-training on multi-scale aerial views is essential to enhance the robustness and effectiveness of segmentation models in remote sensing.
The Innovative VTPSeg Pipeline
To overcome these challenges, the researchers propose a novel pipeline called VTPSeg (Vision-Text Pretraining for Segmentation). VTPSeg combines the strengths of multiple models, namely Grounding DINO (GD+), CLIP Filter++ (CLIP++), and FastSAM, to achieve enhanced open-vocabulary image segmentation.
- The Grounding DINO+(GD+) module is responsible for generating initial candidate bounding boxes. This module leverages the power of pre-training on diverse visual and textual data to identify potential objects in remote sensing imagery.
- The CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes. By incorporating both visual and textual cues, CLIP++ ensures that only pertinent objects are considered for further segmentation.
- Finally, the refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. FastSAM takes into account the local objects often overlooked by CLIP, resulting in more accurate and detailed segmentation in multi-target remote sensing imagery.
Impact and Future Directions
The VTPSeg pipeline offers a significant advancement in pixel-level segmentation for remote sensing. By addressing the challenges specific to this field, the pipeline holds promise for improving the efficiency and accuracy of object segmentation in remote sensing imagery.
The multi-disciplinary nature of VTPSeg is noteworthy. It combines techniques from computer vision (CLIP and FastSAM) with language understanding (CLIP) and pre-training methodologies (Grounding DINO). This integration of diverse disciplines enhances the capabilities of the pipeline and opens up opportunities for cross-pollination of ideas between different fields.
Furthermore, the concept of enhanced open-vocabulary image segmentation, as demonstrated by VTPSeg, aligns with the wider field of multimedia information systems. Multimedia information systems deal with the management, retrieval, and analysis of multimedia data, including images and videos. Accurate segmentation is vital for efficient indexing and retrieval of multimedia content, making VTPSeg relevant not only for remote sensing but also for various multimedia applications.
Looking ahead, future research can explore the application of VTPSeg to other domains beyond remote sensing. The pipeline’s modular design allows for potential adaptability to different types of images and datasets. Additionally, incorporating more sophisticated techniques for post-processing and refinement of segmentation results could further improve the accuracy and usability of VTPSeg.
In conclusion, the VTPSeg pipeline presents a promising approach to enhance open-vocabulary image segmentation in remote sensing. By leveraging the strengths of different models and addressing the specific challenges of this field, VTPSeg contributes to the wider field of multimedia information systems and paves the way for future advancements in object recognition and segmentation.