Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques (2410.17283v2)

Abstract: Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in AI, and the advancements in visual LLMs (VLMs) have pushed this enthusiasm to new heights. Differring from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they addressed. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods. A project associated with this review has been created at https://github.com/taolijie11111/VLMs-in-RS-review.

Advancements in Visual LLMs for Remote Sensing

The paper at hand provides an exhaustive survey of recent advancements in Visual LLMs (VLMs) as applied to remote sensing (RS) data, with a focus on three core areas: datasets dedicated to VLMs, model capabilities, and enhancement techniques. VLMs represent a significant shift from traditional discriminative models to generative models, integrating linguistic context with visual information. This synthesis offers potential solutions to various complex remote sensing tasks that were previously constrained by the limitations of purely visual models.

Datasets for Visual LLMs in Remote Sensing

The paper presents a comprehensive compilation of datasets that support VLM development in the remote sensing domain. These datasets are categorized into three types: manual, combining existing datasets, and enhanced by VLMs and LLMs.

1. Manual Datasets: These datasets, though limited in size, are carefully annotated for specific tasks, ensuring high data quality. Examples include HallusionBench, RSICap, and CRSVQA, which are meticulously crafted for tasks like visual question answering and image captioning.
2. Combining Existing Datasets: These datasets merge several existing domain-specific datasets to facilitate multi-task models, such as SATIN and SkyEye-968K. While expansive, these datasets compromise on annotation quality compared to manual datasets.
3. Automatically Annotated Datasets: Leveraging state-of-the-art models like CLIP and GPT, these datasets attain large scale and quality with minimal manual intervention. Examples include RS5M and SkyScript, which employ generative and contrastive techniques to produce high-quality image-text pairs, supporting diverse applications in remote sensing.

Capabilities of Visual LLMs

The transformation ushered in by VLMs enables handling an array of tasks in remote sensing that extend beyond traditional image analysis into multi-modal comprehension:

• Pure Visual Tasks: VLMs enhance standard tasks such as scene classification (SC), object detection (OD), and semantic segmentation (SS), due to their deep understanding of the visual context.
• Visual Language Tasks: VLMs are highly capable in tasks that require text-visual integration, like image captioning (IC), visual question answering (VQA), and change detection (CD). This integration allows for complex, interactive applications that mimic human-like reasoning in understanding visual content.

Enhancement Techniques for Visual LLMs

The paper categorizes improvements in VLMs into those focused on contrastive learning and conversational frameworks:

1. Contrastive Learning Models: These rely on aligning visual and text features to enhance model understanding. Examples include RemoteCLIP and ChangeCLIP, which focus on enhancing feature alignment through continued learning frameworks.
2. Conversational Models: These models, such as SkySenseGPT and GeoChat, integrate visual encoders with LLMs (such as the LLaMA series) to execute complex tasks requiring nuanced human-like dialogue capabilities in interpreting remote sensing imagery. These models outperform contrastive approaches in tasks involving natural language understanding.

Implications and Future Directions

The integration of VLMs into remote sensing demonstrates significant potential in both theoretical insights and practical applications, such as environmental monitoring and disaster management. However, the paper identifies key areas needing further research:

• Addressing Regression Tasks: Current approaches show limitations in handling tasks involving regression outputs, suggesting a need for frameworks that can interpret numerical data in conjunction with visual models.
• Exploiting Structural Characteristics of RS Data: Enhancing models to better capture remote sensing image features that are often distinct from traditional RGB datasets can yield considerable performance improvements.
• Multimodal Outputs: Developing capabilities for models to generate multi-format outputs, including images and potentially video, could revolutionize applications in dense prediction tasks like segmentation.

In conclusion, the paper positions VLMs as pivotal to advancing remote sensing methodologies, offering significantly expanded interpretive capacities and cross-modal task handling. The future of VLMs in remote sensing lies in continuing to research modalities specific to remote sensing data and improving integrative approaches to further bridge visual and linguistic data understanding.