EFFC-Net: lightweight fully convolutional neural networks in remote sensing disaster images

Abstract

ABSTRACT Continuous development of remote sensing technology can rapidly and accurately extract secondary disaster information, such as the area of various disasters. However, in the extraction process, some disasters should be initially classified and identified. In view of this concept, a lightweight fully Convolutional Neural Network (CNN) model Earthquake – Flood–Fire – Cyclone (EFFC)-Net is proposed. Two modules, EFFC_Block and EFFC_Tran_Block, which are used for feature extraction and feature transformation, respectively, are introduced. The EFFC-Net network model is reconstructed through the EFFC_Block and EFFC_Tran_Block modules. Subsequently, EFFC-Net is compared with CNN and transformer models. Results show that the EFFC-Net network model performs effectively in precision, recall, F1_score, and parameters, outperforming the more advanced CNN and transformer models. Moreover, the test time of the Cifar_10 and Cifar_100 datasets was compared, and the results indicate that the EFFC-Net algorithm has the shortest running time and achieves the lightweight goal. Therefore, the EFFC-Net lightweight fully CNN has high disaster classification application value and good portability.