.png){kind=icon}
返回
MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2021.3106391.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
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
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network基于关键滤波器组的卷积神经网络高分辨率遥感影像场景分类
PREAI
分享收藏Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏
查看更多内容