MGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification
This work provides an incremental improvement in remote sensing scene classification accuracy for researchers and practitioners working with satellite imagery.
This paper addresses remote sensing scene classification, which is challenged by large intra-class variance and confusing information in images. The authors propose MGML-FENet, a network that extracts and ensembles multi-granularity, multi-level features, achieving better performance than previous state-of-the-art networks on AID, NWPU-RESISC45, UC-Merced, and VGoogle datasets.
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.