In recent years

few-shot object detection (FSOD) in remote sensing images (RSIs) has garnered significant attention from the research community. This technique aims to empower object detectors to identify novel object categories using a minimal number of labeled samples within RSIs. The importance of this research is underscored by the vast amounts of unlabeled remote sensing data available

making the efficient utilization of limited labeled data crucial. The core challenge in FSOD for RSIs lies in the requirement for base class samples during the learning process of novel class knowledge. In practical scenarios

object categories are constantly evolving and adapting to changing environments. This dynamic nature poses a significant obstacle for traditional FSOD methods

as they rely heavily on base class samples to learn novel class. However

such a dependency limits the ability of these methods to adapt tonovel class indefinitely. Furthermore

as the number of learned categories accumulates

the memory usage

storage requirements

and computational demands increase significantly. This scalability issue can become a major bottleneck

especially when dealing with large-scale remote sensing datasets. To address these challenges

we propose a class-incremental few-shot object detection method based on distillation response in remote sensing images (DR-CFSOD). This innovative approach differs significantly from existing methods by enabling the learning of novel class independently

without relying on base class. This crucial aspect alleviates the need for continuously incorporating base class samples

thereby overcoming the limitations of traditional FSOD methods. Moreover

DR-CFSOD addresses the catastrophic forgetting problem that often occurs in incremental learning scenarios. By distilling the knowledge learned from previous classes

the algorithm preserves the discriminative ability of the model

ensuring that it can accurately detect both base and novel class. To validate the effectiveness of our proposed method

we conducted extensive experiments on two diverse remote sensing image datasets. The results obtained demonstrate the superior performance of DR-CFSOD compared to baseline methods. Our method not only achieves competitive detection accuracy but also exhibits superior scalability and adaptability to new categories. Concurrently

our proposed method boasts an impressive 95FPS real-time detection performance

ensuring swift and accurate identification. This outstanding feature provides a solid foundation for seamless integration and effective application in practical scenarios

further enhancing its utility and value.