MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification

作     者：Zhao, Qi Lyu, Shuchang Li, Yuewen Ma, Yujing Chen, Lijiang 

作者机构：Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 (IEEE Trans. Neural Networks Learn. Sys.)

年 卷 期：2023年第34卷第5期

页      面：2308-2322页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [YWF-21-BJ-J-534] 

主　　题：Feature extraction Task analysis Learning systems Fuses Generators Data mining Remote sensing Channel-separate feature generation (CS-FG) feature ensemble network full-channel feature generation (FC-FG) multigranularity multilevel feature representation remote sensing (RS) scene classification 

摘      要：Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main issues: large intraclass variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two issues. We propose a multigranularity multilevel feature ensemble network (MGML-FENet) to efficiently tackle the RS scene classification task in this article. Specifically, we propose multigranularity multilevel feature fusion branch (MGML-FFB) to extract multigranularity features in different levels of network by channel-separate feature generator (CS-FG). To avoid the interference from confusing information, we propose a multigranularity multilevel feature ensemble module (MGML-FEM), which can provide diverse predictions by full-channel feature generator (FC-FG). Compared to previous methods, our proposed networks have the ability to use structure information and abundant fine-grained features. Furthermore, through the ensemble learning method, our proposed MGML-FENets can obtain more convincing final predictions. Extensive classification experiments on multiple RS datasets (AID, NWPU-RESISC45, UC-Merced, and VGoogle) demonstrate that our proposed networks achieve better performance than previous state-of-the-art (SOTA) networks. The visualization analysis also shows the good interpretability of MGML-FENet.