Adaptive Multi-Proxy for Remote Sensing Image Retrieval
Adaptive Multi-Proxy for Remote Sensing Image Retrieval
复制标题
自适应多代理遥感图像检索
DOI：
10.3390/rs14215615
复制
发表时间：
2022-11-01
期刊：
影响因子：
5
通讯作者：
Ge, Mengying
中科院分区：
文献类型：
--
作者：
Li, Xinyue;Wei, Song;Ge, Mengying
随着遥感技术的发展，基于内容的遥感图像检索已成为研究热点。遥感影像数据集不仅包含丰富的位置、语义和尺度信息，而且类内差异较大。因此，提高遥感图像检索性能的关键是充分利用有限的样本信息提取更全面的类特征。在本文中，我们提出了一种基于代理的深度度量学习方法和自适应多代理框架。首先，我们提出了一种具有随机因子的簇内样本合成策略，该策略使用批量中的有限样本来合成更多样本，以增强网络对类中不明显特征的学习。其次，我们提出了一种自适应代理分配方法，根据类内样本的聚类分配多个代理，并根据聚类规模确定每个代理的权重，以准确、全面地衡量样本类相似度。最后，我们结合了严格的评估指标 mAP@R 和各种数据集划分方法，并对常用的遥感图像数据集进行了广泛的实验。
With the development of remote sensing technology, content-based remote sensing image retrieval has become a research hotspot. Remote sensing image datasets not only contain rich location, semantic and scale information but also have large intra-class differences. Therefore, the key to improving the performance of remote sensing image retrieval is to make full use of the limited sample information to extract more comprehensive class features. In this paper, we propose a proxy-based deep metric learning method and an adaptive multi-proxy framework. First, we propose an intra-cluster sample synthesis strategy with a random factor, which uses the limited samples in batch to synthesize more samples to enhance the network's learning of unobvious features in the class. Second, we propose an adaptive proxy assignment method to assign multiple proxies according to the cluster of samples within a class, and to determine weights for each proxy according to the cluster scale to accurately and comprehensively measure the sample-class similarity. Finally, we incorporate a rigorous evaluation metric mAP@R and a variety of dataset partitioning methods, and conduct extensive experiments on commonly used remote sensing image datasets.