In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive decoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap.

翻译：近年来，先进研究聚焦于利用自然语言处理技术直接学习与分析遥感图像。准确描述多时相遥感图像中的变化，对于地理空间理解与土地规划日益重要。与自然图像变化描述任务不同，遥感变化描述旨在捕捉最显著的变化，不受光照、季节效应及复杂地表覆盖等因素影响。本研究强调准确描述遥感图像变化的重要性，并对比了自然图像合成图像与遥感图像的变化描述任务。为解决生成准确描述的挑战，我们提出一种针对双时相遥感图像的注意力变化-描述网络（简称Chg2Cap）。该网络包含三个主要部分：1）基于孪生CNN的特征提取器，用于提取每对图像对的高层表示；2）包含层次化自注意力块的注意力解码器，用于定位变化相关特征，以及生成图像嵌入的残差块；3）基于Transformer的描述生成器，用于将图像嵌入与词嵌入之间的关系解码为描述。所提出的Chg2Cap网络在两个代表性遥感数据集上进行了评估，并提供了全面的实验分析。代码与预训练模型将在https://github.com/ShizhenChang/Chg2Cap公开。