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Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
Content Provider | MDPI |
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Author | Li, Zihao Zhang, Daobing Wang, Yang Lin, Daoyu Zhang, Jinghua |
Copyright Year | 2022 |
Description | Deep learning-based methods s쳮d in remote sensing scene classification (RSSC). However, current methods require training on a large dataset, and if a class does not appear in the training set, it does not work well. Zero-shot classification methods are designed to address the classification for unseen category images in which the generative adversarial network (GAN) is a popular method. Thus, our approach aims to achieve the zero-shot RSSC based on GAN. We employed the conditional Wasserstein generative adversarial network (WGAN) to generate image features. Since remote sensing images have inter-class similarity and intra-class diversity, we introduced classification loss, semantic regression module, and class-prototype loss to constrain the generator. The classification loss was used to preserve inter-class discrimination. We used the semantic regression module to ensure that the image features generated by the generator can represent the semantic features. We introduced class-prototype loss to ensure the intra-class diversity of the synthesized image features and avoid generating too homogeneous image features. We studied the effect of different semantic embeddings for zero-shot RSSC. We performed experiments on three datasets, and the experimental results show that our method performs better than the state-of-the-art methods in zero-shot RSSC in most cases. |
Starting Page | 3760 |
e-ISSN | 20763417 |
DOI | 10.3390/app12083760 |
Journal | Applied Sciences |
Issue Number | 8 |
Volume Number | 12 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-04-08 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Remote Sensing Remote Sensing Scene Classification Zero-shot Learning Generative Adversarial Network |
Content Type | Text |
Resource Type | Article |