MFST: A Multi-Level Fusion Network for Remote Sensing Scene Classification

Guoqing Wang, Ning Zhang, Wenchao Liu*, He Chen, Yizhuang Xie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Scene classification has become an active research area in remote sensing (RS) image interpretation. Recently, Transformer-based methods have shown great potential in modeling global semantic information and have been exploited in RS scene classification. In this letter, we propose a multi-level fusion Swin Transformer (MFST), which integrates a multi-level feature merging (MFM) module and an adaptive feature compression (AFC) module to further boost the performance for RS scene classification. The MFM module narrows the semantic gaps in multi-level features via patch merging in lower-level feature maps and lateral connections in the top-down pathway. The AFC module makes multi-level features have smaller dimensions and more coherent semantic information by adaptive channel reduction. We evaluate the proposed network on the aerial image dataset (AID) and NWPU-RESISC45 (NWPU) datasets, and the classification results reveal that the proposed network outperforms several state-of-the-art (SOTA) methods.

Original languageEnglish
Article number6516005
JournalIEEE Geoscience and Remote Sensing Letters
Volume19
DOIs
Publication statusPublished - 2022

Keywords

  • Feature fusion
  • Transformer
  • multi-level
  • remote sensing (RS) scene classification

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