MULTI-SCALE CROPPING MECHANISM FOR REMOTE SENSING IMAGE CAPTIONING

With the rapid development of artificial satellite, a large number of high resolution remote sensing images can be easily obtained now. Recently, remote sensing image captioning, which aims to generate accurate and concise descriptive sentences for remote sensing images, has been promoted by template-based model and encoder-decoder model with several related datasets released. Based on an encoder-decoder model, we propose a training mechanism of multi-scale cropping for remote sensing image captioning in this paper, which can extract more fine-grained information from remote sensing images and enhance the generalization performance of the base model. The experimental results on two datasets UCM-captions and Sydney-captions demonstrate that the proposed approach availably improves the performances in describing high resolution remote sensing images.