Captioning Changes in Bi-Temporal Remote Sensing Images

Motivated by the good performance recorded for image captioning (IC) techniques in different remote sensing (RS) applications, we propose in this paper a change detection (CD) system based on IC. It aims to the creation of a user-friendly solution that describes, via human-like sentences, the changes detected comparing bi-temporal images acquired over the same geographical area. The model we propose, is based on a convolutional neural network (CNN), and a multimodal recurrent neural network (RNN). Our experiments have been performed combining a set of aerial images with semantic information that we generated to describe the changes observed for different types of objects. This work yielded to encouraging results, evaluated using the BLEU metric.