Disaster cassification net: A disaster classification algorithm on remote sensing imagery - Yuan, Jianye; Ding, Xinwang; Liu, Fangyuan; Cai, Xin (Frontiers in Environmental Science, 2023)
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.