  Remote sensing (RS) scene classification is a challenging task to predict
scene categories of RS images. RS images have two main characters: large
intra-class variance caused by large resolution variance and confusing
information from large geographic covering area. To ease the negative influence
from the above two characters. We propose a Multi-granularity Multi-Level
Feature Ensemble Network (MGML-FENet) to efficiently tackle RS scene
classification task in this paper. Specifically, we propose Multi-granularity
Multi-Level Feature Fusion Branch (MGML-FFB) to extract multi-granularity
features in different levels of network by channel-separate feature generator
(CS-FG). To avoid the interference from confusing information, we propose
Multi-granularity Multi-Level 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 ability to use structure
information and abundant fine-grained features. Furthermore, through 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.
