Article
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Preserved in Portico This version is not peer-reviewed
Remote Sensing Image Harmonization Method for Fine Grained Ship Classification
Version 1
: Received: 21 May 2024 / Approved: 22 May 2024 / Online: 28 May 2024 (09:14:52 CEST)
A peer-reviewed article of this Preprint also exists.
Zhang, J.; Zhong, Z.; Wei, X.; Wu, X.; Li, Y. Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification. Remote Sens. 2024, 16, 2192. Zhang, J.; Zhong, Z.; Wei, X.; Wu, X.; Li, Y. Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification. Remote Sens. 2024, 16, 2192.
Abstract
Target recognition and fine-grained ship classification in remote sensing face the challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of region aware instantiation and attention mechanism. Implement adversarial learning through image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the do-main encoder and domain discriminator modules are responsible for extracting feature representations of foreground and background, and further aligning ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on 8 typical fine-grained classification algorithms, and the accuracy of fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.
Keywords
Remote Sensing; Fine-grained Ship Classification; Transfer Learning; Image Synthesis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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