Through research and analysis of the existing remote sensing image sharing and distribution systems, remote sensing image recommendation mode can be divided into subscription recommendation and active recommendation. The first mode provides data query retrieval and subscription distribution services. However, retrieval and subscription services are based on query and subscription keywords, which are problematic or insufficiently active for users. Moreover, these processes cannot discover the latent requirements of a user. Therefore, how to recommend remote sensing images to users accurately and actively is a challenging problem. Research on the active remote sensing image recommendation is rare. The typical method is a space–time periodic task model (STPT), which realizes personalized remote sensing image recommendation based on simulation user log records. However, STPT is not accurate enough because it uses the minimum bounding rectangle as the filter condition of spatial feature and considers that the user’s acquisition of images is periodic, so the data that match the periodic rules is more likely to be returned, resulting in a low recall rate. Additionally, it is less efficient for large-scale image recommendation tasks because it takes a long time to calculate the time-period using Fourier transform method. In this study, we propose a spatial–temporal embedding topic (STET) model to solve the recommendation problem of remote sensing images. This model processes the spatial, temporal, and content information of remote sensing images and constructs a topic model, thereby fully applying the continuity characteristics of space and time and improving the training efficient of the recommendation model. Compared with state-of-the-art models, the results based on large scale real-world datasets show that our model not only significantly improves the recall by more than 10%, the normalized discounted cumulative gain by more than 10% when K is 100 with the precision remaining above 97%, but it also greatly reduces the training time.

通过对现有遥感影像共享分发系统的研究和分析，遥感影像推荐模式可分为订阅式推荐和主动式推荐。第一种模式提供数据查询检索和订阅分发服务。然而，检索和订阅服务是基于查询和订阅关键字，这对用户来说存在问题或不够活跃。此外，这些过程无法发现用户的潜在需求。因此，如何准确主动地向用户推荐遥感影像是一个具有挑战性的问题。主动遥感影像推荐的研究很少。典型的方法是时空周期任务模型（STPT），实现基于模拟用户日志记录的个性化遥感影像推荐。但是，STPT不够准确，因为它使用最小包围矩形作为空间特征的过滤条件，并考虑到用户对图像的获取是周期性的，所以更容易返回符合周期性规则的数据，导致召回率低。此外，对于大规模图像推荐任务效率较低，因为使用傅立叶变换方法计算时间段需要很长时间。在这项研究中，我们提出了一种时空嵌入主题（STET）模型来解决遥感图像的推荐问题。该模型对遥感图像的空间、时间、内容信息进行处理，构建主题模型，从而充分利用空间和时间的连续性特点，提高推荐模型的训练效率。与最先进的模型相比，基于大规模真实世界数据集的结果表明，我们的模型不仅显着提高了 10% 以上的召回率，当 K 为100 精度保持在 97% 以上，但也大大减少了训练时间。