EFM-Net: An Essential Feature Mining Network for Target Fine-Grained Classification in Optical Remote Sensing Images

Target fine-grained classification has been the research hotspot in remote sensing image interpretation, which has received general attention in application fields. One challenge of the fine-grained classification task is to learn the most discriminative feature using the deep convolutional neural network (DCNN). At present, many works of fine-grained image classification obtain target features by optimizing the feature extraction and enhancement, which are not accurate enough in remote sensing images. In this article, we propose an essential feature mining network (EFM-net) based on DCNN to address this issue. Its major motivation is to obtain the essential feature, which is fine enough to distinguish between similar instances. The proposed pipeline includes the Miner for purifying the essential feature and the Refiner for data augmentation. These two modules can work in a mutually reinforcing way and extract the essential feature of targets. We evaluate EFM-Net on two public fine-grained classification datasets in remote sensing, FGSC-23 and FGSCR-42, and our Aircraft-16. The results show that the proposed method consistently outperforms existing alternatives. We have released our source code in GitHub https://github.com/JACYI/EFM-Net-Pytorch.git.

Automated summary

In this article, we propose an essential feature mining network (EFM-net) based on deep convolutional neural network (DCNN) to obtain discriminative features for fine-grained classification in remote sensing. The proposed EFM-net includes two modules, Miner and Refiner, which work together to extract essential features of the targets. Experimental results show the superiority of the proposed method over existing alternatives on public datasets.