基于内容解译的遥感图像推荐方法

Remote sensing image recommendation method based on content interpretation

随着遥感技术的不断发展,遥感数据呈现出海量增多的趋势,如何提供精准及时的遥感信息推荐服务成为亟待解决的问题。现有的遥感图像推荐算法大多针对用户画像进行设计,忽视了图像内容的语义信息对推荐结果的影响。针对上述问题,提出一种基于内容解译的遥感图像推荐方法。首先,通过基于YOLOv3的目标检测模块对遥感图像进行目标提取;然后,整合关键目标的位置分布向量作为图像内容信息;同时,构建多元素的用户兴趣画像,并根据用户主动搜索历史进行动态调整,以提高推荐结果的个性化程度;最后,将图像内容信息与图像自带属性信息、用户画像模型进行匹配,实现遥感数据的精准智能推荐。在真实订单数据上与较新的仅基于图像属性信息的推荐方法进行对比实验,实验结果表明,所提方法在实验数据上取得的正负样本区分度比考虑用户画像的推荐方法提高了70%;在耗时基本相近的情况下,在使用10%训练数据时,推荐错误率与对比方法相比下降了4.0~5.6个百分点,而在使用100%训练数据时推荐错误率则下降了0.6~1.0个百分点,验证了所提方法的可行性与有效性。

With the continuous development of remote sensing technology,there has been a significant increase in the volume of remote sensing data.Providing accurate and timely remote sensing information recommendation services has become an urgent problem to solve.Existing remote sensing image recommendation algorithms mainly focus on user portrait,overlooking the influence of image content semantics on recommendation results.To address these issues,a remote sensing image recommendation method based on content interpretation was proposed.Firstly,an object extraction module based on YOLOv3 was used to extract objects from remote sensing images.Then,the location distribution vectors of key objects were integrated as image content information.Additionally,a multi-element user interest portrait was constructed and dynamically adjusted based on the user’s active search history to enhance the personality of recommendation results.Finally,the image content information was matched with the inherent attribute information of image and the user profile model to achieve accurate and intelligent recommendations of remote sensing data.Comparative experiments were conducted on real order data,to compare the proposed method with the newer recommendation method based solely on image attribute information.Experimental results show that the proposed method achieves a 70% improvement in the discrimination of positive and negative samples on the experimental data compared to the recommendation method considering user portrait.When using 10% training data with similar consumption time,the recommendation error rate decreases by 4.0-5.6 percentage points compared to the comparison method.When using 100% training data,the recommendation error rate decreases by 0.6-1.0 percentage points.These results validate the feasibility and effectiveness of the proposed method.