PReLim: A Modeling Paradigm for Remote Sensing Image Scene Classification Under Limited Labeled Samples

Document Type

Conference Article

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

Abstract

With the ongoing development of deep learning techniques in recent years, the convolutional neural networks (CNNs) have shown remarkable performance breakthrough in remote sensing image scene classification. However, the performance of these deep models largely depends on the number of available training samples or labeled images. Although the knowledge transferring and pre-training techniques can handle such situation, these may become ineffective due to domain difference. On the other side, the existing data augmentation approaches often produce training samples with too low diversity to help in performance improvement. In order to address these issues, in this work, we propose PReLim as a novel modeling paradigm for remote sensing scene classification under limited labeled samples scenario. PReLim is based on the notion of local and global filtering of scene fragment mixture, which overcomes both the sample diversity and the domain difference issue. Experimental analyses with the benchmark UCMerced and SIRI-WHU datasets demonstrate the effectiveness of PReLim in achieving the state-of-the-art accuracy using limited number of training samples.

First Page

545

Last Page

555

DOI

10.1007/978-3-031-12700-7_56

Publication Date

1-1-2024

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