Diverse Text-Prompt Generation for Remote Sensing Image Classification

Inadequate remote sensing image training data usually makes remote sensing image classification models achieve low accuracy. Thus, we propose a diverse text-prompt generation learning (DPL) method. The context optimization (CoOp) model transfers the feature extraction capabilities of the CLIP model to downstream tasks with learnable text prompts. However, the small number of samples in remote sensing images can easily lead to noise. In contrast, DPL introduces a diverse text-prompt generation structure. Due to the diversity of multiple prompts, noise generated by inadequate samples is suppressed. Moreover, in order to keep the prompts diverse, we propose a prompt diversity loss. This loss pulls the prompts away from each other, which suppresses the noise generated by the limited training samples. Extensive experiments show that our method achieves superior performance than the existing methods on DOTA, HRRSD, and NWPU VHR-10 datasets. The model and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LvXiangzhu/DPL</uri>.