Toward Zero-Forgetting: A Training-Free Multimodal Framework for Remote Sensing Class-Incremental Learning

Abstract

Existing class-incremental learning (CIL) methods for remote sensing (RS) scene classification often tend to be training-intensive or rely on static visual features that may inadequately capture the complex interclass similarity and intraclass diversity inherent in RS imagery. Moreover, directly reusing features from models pretrained on natural images (e.g., CLIP) often suffers from a domain gap and limited semantic richness when transferred to the RS domain. In this article, we propose a simple yet effective training-free CIL framework for RS scene classification that leverages multimodal semantic information to build more discriminative category representations. Our framework treats pretrained models as frozen feature extractors to guarantee zero forgetting of the representation space. To enhance semantic discriminability, we employ a large-language model (LLM) to generate rich candidate textual descriptions for each class and introduce an image-guided description selection (IGDS) strategy to align semantic information with visual characteristics. Classification is performed using a distance-based metric without any additional training. Extensive experimental results demonstrate that our framework achieves leading performance and superior stability across different session sequences, surpassing both training-based and training-free baseline methods. Our code is available at https://github.com/WenliangDu/ZFCIL-RS