Remote Sensing Scene Classification Based on Multibranch Fusion Attention Network

Scene classification plays a significant role in the field of remote sensing (RS). Recently, the rapid development of convolutional neural networks (CNNs) has enabled a vital breakthrough in high-resolution RS image scene classification. However, complex backgrounds and small objects in high-resolution RS images pose challenges to the application of CNNs. To this end, a novel multibranch fusion attention network (MBFANet) is proposed to improve the feature extraction ability and generalization performance of models. Specifically, a multibranch fusion attention module (MBFAM) is designed by adaptively fusing two parallel submodules, namely, efficient pooling channel attention module (EPCAM) and efficient convolution coordinate attention module (ECCAM), which helps the model focus on more key cues in images that are difficult to classify. The ablation experiments in RS scene classification datasets demonstrate the effectiveness of our methods. In addition, MBFANet achieves competitive results on three benchmark datasets.