A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection (2312.01163v2)

Abstract: Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover. Although numerous deep learning-based CD models have performed excellently, their further performance improvements are constrained by the limited knowledge extracted from the given labelled data. On the other hand, the foundation models that emerged recently contain a huge amount of knowledge by scaling up across data modalities and proxy tasks. In this paper, we propose a Bi-Temporal Adapter Network (BAN), which is a universal foundation model-based CD adaptation framework aiming to extract the knowledge of foundation models for CD. The proposed BAN contains three parts, i.e. frozen foundation model (e.g., CLIP), bi-temporal adapter branch (Bi-TAB), and bridging modules between them. Specifically, BAN extracts general features through a frozen foundation model, which are then selected, aligned, and injected into Bi-TAB via the bridging modules. Bi-TAB is designed as a model-agnostic concept to extract task/domain-specific features, which can be either an existing arbitrary CD model or some hand-crafted stacked blocks. Beyond current customized models, BAN is the first extensive attempt to adapt the foundation model to the CD task. Experimental results show the effectiveness of our BAN in improving the performance of existing CD methods (e.g., up to 4.08\% IoU improvement) with only a few additional learnable parameters. More importantly, these successful practices show us the potential of foundation models for remote sensing CD. The code is available at \url{https://github.com/likyoo/BAN} and will be supported in our Open-CD.

• The paper introduces BAN, which integrates a frozen foundation model with a bi-temporal adapter branch to enhance change detection tasks.
• It demonstrates significant performance gains with up to a 4.08% IoU improvement on the LEVIR-CD dataset and consistent results on other benchmarks.
• BAN’s design requires minimal additional parameters, offering a scalable and efficient solution for remote sensing change detection in data-scarce environments.

A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection

The paper "A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection" by Kaiyu Li et al. introduces an innovative framework for the deployment of foundation models in change detection (CD) tasks within remote sensing. This work proposes the Bi-Temporal Adapter Network (BAN), a novel paradigm designed to harness the extensive general knowledge embedded in foundation models, such as CLIP, to enhance existing CD methodologies.

Key Contributions

The BAN architecture is composed of three primary components: a frozen foundation model, a bi-temporal adapter branch (Bi-TAB), and bridging modules. The foundation model operates in a frozen state, extracting general features that are independent of specific tasks. These features are then filtered, aligned, and integrated into the Bi-TAB via bridging modules. The Bi-TAB can be any existing CD model, making BAN adaptable to various CD methodologies. This flexibility allows BAN to be easily integrated with and improve the performance of current CD models such as BiT and ChangeFormer.

Experimental Results

Empirical evaluations indicate substantial improvements in CD performance across multiple datasets. On the LEVIR-CD dataset, BAN achieved an impressive increase in Intersection over Union (IoU) of up to 4.08% compared to baseline models. This enhancement was consistent across other datasets, including S2Looking and BANDON, which further solidifies the efficacy of leveraging foundation models in CD tasks.

The authors demonstrate that BAN requires only a modest increase in learnable parameters while delivering significant performance gains. This is particularly advantageous in scenarios with limited labeled data, where the transfer of general knowledge from foundation models can compensate for data scarcity.

Theoretical and Practical Implications

From a theoretical standpoint, the BAN framework embodies a step towards a more generalized approach to CD by utilizing foundation models. This indicates a shift in the domain from relying on task-specific data to employing large-scale, pre-trained models to transfer knowledge across domains effectively. Practically, BAN suggests a paradigm where future developments in CD can be bolstered by advances in foundation models, thus minimizing the reliance on collecting and labeling large datasets specific to CD tasks.

Future Directions

The paper posits several promising directions for future research. These include exploring more efficient parameter-efficient transfer learning (PETL) methods, enhancing the alignment between generic and task-specific features, and further extending BAN to diverse data types such as multispectral and hyperspectral images. Additionally, as foundation models evolve and increase in capability, their integration into BAN holds potential for further elevating CD performance across more challenging and less-structured environmental data.

In conclusion, Li et al.'s work on BAN demonstrates a significant stride in the application of foundation models to the domain of remote sensing, offering a scalable, efficient, and adaptable framework to enhance change detection capabilities amidst the inherent challenges posed by limited data availability.