Multigranularity Decoupling Network With Pseudolabel Selection for Remote Sensing Image Scene Classification

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 13
• Main Authors: Miao, Wang, Geng, Jie, Jiang, Wen
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Data models; Datasets; Decoupling; Feature extraction; Image classification; Imbalanced learning (IL); Remote sensing; remote sensing image; scene classification; Semisupervised learning; semisupervised learning (SSL); Tail; Task analysis; Training
• DOI: 10.1109/TGRS.2023.3244565
• ISSN: 0196-2892

The existing deep networks have shown excellent performance in remote sensing scene classification (RSSC), which generally requires a large amount of class-balanced training samples. However, deep networks will result in underfitting with imbalanced training samples since they can easily bias toward the majority classes. To address these problems, a multigranularity decoupling network (MGDNet) is proposed for remote sensing image scene classification. To begin with, we design a multigranularity complementary feature representation (MGCFR) method to extract fine-grained features from remote sensing images, which utilizes region-level supervision to guide the attention of the decoupling network. Second, a class-imbalanced pseudolabel selection (CIPS) approach is proposed to evaluate the credibility of unlabeled samples. Finally, the diversity component feature (DCF) loss function is developed to force the local features to be more discriminative. Our model performs satisfactorily on three public datasets: UC Merced (UCM), NWPU-RESISC45, and Aerial Image Dataset (AID). Experimental results show that the proposed model yields superior performance compared with other state-of-the-art methods.