摘要
In existing remote sensing image retrieval (RSIR) datasets, the number of images among different classes varies dramatically, which leads to a severe class imbalance problem. Some studies propose to train the model with the ranking-based metric (e.g., average precision [AP]), because AP is robust to class imbalance. However, current AP-based methods overlook an important issue: only optimising samples ranking before each positive sample, which is limited by the definition of AP and is prone to local optimum. To achieve global optimisation of AP, a novel method, namely Optimising Samples after positive ones & AP loss (OSAP-Loss) is proposed in this study. Specifically, a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper bound. Then, a novel loss called Optimising Samples after Positive ones (OSP) loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each sample. Finally, a graphics processing unit memory-free mechanism is developed to thoroughly address the non-decomposability of AP optimisation. Extensive experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP-Loss compared to the state-of-the-art.