MGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification

• URL: http://arxiv.org/abs/2012.14569v1
• Date: Tue, 29 Dec 2020 02:18:11 GMT
• Title: MGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification
• Authors: Qi Zhao, Shuchang Lyu, Yuewen Li, Yujing Ma, Lijiang Chen
• Abstract summary: We propose a Multi-granularity Multi-Level Feature Ensemble Network (MGML-FENet) to efficiently tackle RS scene classification task. We show that our proposed networks achieve better performance than previous state-of-the-art (SOTA) networks.
• Score: 15.856162817494726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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