Bridging the MiniBatch and Adversarial Optimal Transport for Cross-Scene Classification of Remote Sensing Smoke-Related Scenes

Smoke in remote sensing (RS) images is regarded as the indicator of fire disasters and it is vital to distinguish smoke from other RS scenes. Convolutional neural network (CNN) based classification networks have viewed great success but the cross-entropy loss may suffer from domain gap when training data and testing data do not follow independent and identical distributions. Served as an objective function, the optimal transport (OT) distance is able to tackle the domain gap by aligning two distributions in domain adaptation tasks. Generally speaking, the OT-based algorithms can be divided into two categories: methods that compute OT distance on minibatches, and methods which rely on the adversarial training. In this letter, we propose a hybrid OT algorithm, termed Joint MiniBatch and Adversarial OT (JMBAOT), to eliminate the domain gap for cross-scene classification of RS smoke-related scenes. JMBAOT takes advantage of both the minibatch and adversarial OT to better distinguish different classes on the feature space and improve the performance of domain adaptation. In a large amount of experiments, JMBAOT shows its effectiveness and achieves the state-of-the-art (SOTA) performance.