Triplet-Metric-Guided Multi-Scale Attention for Remote Sensing Image Scene Classification with a Convolutional Neural Network

Remote sensing image scene classification (RSISC) plays a vital role in remote sensing applications. Recent methods based on convolutional neural networks (CNNs) have driven the development of RSISC. However, these approaches are not adequate considering the contributions of different features to the global decision. In this paper, triplet-metric-guided multi-scale attention (TMGMA) is proposed to enhance task-related salient features and suppress task-unrelated salient and redundant features. Firstly, we design the multi-scale attention module (MAM) guided by multi-scale feature maps to adaptively emphasize salient features and simultaneously fuse multi-scale and contextual information. Secondly, to capture task-related salient features, we use the triplet metric (TM) to optimize the learning of MAM under the constraint that the distance of the negative pair is supposed to be larger than the distance of the positive pair. Notably, the MAM and TM collaboration can enforce learning a more discriminative model. As such, our TMGMA can avoid the classification confusion caused by only using the attention mechanism and the excessive correction of features caused by only using the metric learning. Extensive experiments demonstrate that our TMGMA outperforms the ResNet50 baseline by 0.47% on the UC Merced, 1.46% on the AID, and 1.55% on the NWPU-RESISC45 dataset, respectively, and achieves performance that is competitive with other state-of-the-art methods.