Exploring Models and Data for Remote Sensing Image Caption Generation
This work addresses the challenge of automated captioning for remote sensing images, which is incremental as it builds on existing methods by introducing new data and annotations.
The paper tackles the problem of generating accurate and concise captions for remote sensing images by constructing a large-scale annotated dataset and providing guidelines, demonstrating that language descriptions can effectively describe image content through experiments.
Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal