AUTHOR=Yuan Jianye , Ding Xinwang , Liu Fangyuan , Cai Xin 

TITLE=Disaster cassification net: A disaster classification algorithm on remote sensing imagery

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1095986

DOI=10.3389/fenvs.2022.1095986

ISSN=2296-665X

ABSTRACT=As we all know, natural disasters have a great impact on people's lives and properties, and it is very necessary to deal with disaster categories in a timely and effective manner. Therefore, we propose a new disaster classification network D-Net. During the experiment, we compared the proposed method with "CNN" and "Transformer", and found that D-Net achieves the effect of SOTA on the disaster dataset; After that, we compared the above-mentioned MobileNet_v2 with the best performance on the classification dataset and CCT network are compared with D-Net on fashion_mnist and CIFAR_100 public datasets, respectively, and the results show that D-Net can still achieve the SOTA classification effect. Therefore, our proposed algorithm can be applied not only to disaster tasks, but also to other classification tasks.