# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 4 Conclusions  
This study presents a novel method for zero-shot remote sensing scene classification (RSSC) named CSPWGAN. We are the first to apply generative adversarial networks for zero-shot RSSC. Since the remote sensing image dataset does not directly provide class attribute information, we used four natural language processing models pre-trained on Wikipedia to obtain word vectors as the class semantic information we need. We used generative adversarial networks to train a generator that can generate class image features through class semantic features, converting the zero-shot RSSC problem into a traditional classification problem. Following the characteristics of remote sensing images, we used the 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 a semantic regression module to ensure the image features generated by the generator can represent the semantic features. We introduced class-prototype loss when training the generator to constrain itself to ensure intra-class diversity of the synthesized image features and avoid generating too homogeneous image features. We conducted experiments on two benchmark datasets. The results demonstrate the superiority of our proposed CSPWGAN method for remote sensing images. Our method can work better with a high ratio of unseen classes. In the experiments, we found that the class semantic information significantly affects the classification results. In future work, we will try to obtain better class semantic vectors and use a better approach to reduce classification standard deviation and improve the classification accuracy for remote sensing images.  
In future work, we can also try to use active learning [41] to solve the problem of unseen remote sensing image classes in the training data. The key idea of active learning (AL) is that if a machine-learning algorithm is allowed to select the data it learns, then it can achieve higher accuracy with fewer labeled training instances. Active learning retrieves the most useful unlabeled data from a large set of unlabeled data, hands it over to a professional for labeling, and then uses that sample to train the model to improve its accuracy. Many excellent methods have emerged in the field of remote sensing and deep learning using active learning, such as [42; 43; 44]. Our approach uses semantic and visual information from all image classes. Class semantic information builds a bridge of knowledge transfer between seen and unseen categories, breaking the boundary of category mutual exclusion between training and testing sets. Active learning and our model are two different ideas for solving the problem of addressing unseen classes in the training data.  
Conceptualization, Z.L., D.Z., Y.W. and D.L.; methodology, Z.L. and D.Z.; software, Z.L., Y.W. and J.Z.; validation, Z.L., D.L. and J.Z.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L. and J.Z.; data curation, Z.L. and D.Z.; writing--original draft preparation, Z.L., Y.W. and D.L.; writing--review and editing, Z.L. and D.Z.; visualization, Z.L.; supervision, D.Z.; project administration, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement:[[http://weegee.vision.ucmerced.edu/datasets/landuse.html](http://weegee.vision.ucmerced.edu/datasets/landuse.html)]([http://weegee.vision.ucmerced.edu/datasets/landuse.html](http://weegee.vision.ucmerced.edu/datasets/landuse.html)) (accessed on 7 April 2022), [[https://captain-whu.github.io/AID/](https://captain-whu.github.io/AID/)]([https://captain-whu.github.io/AID/](https://captain-whu.github.io/AID/)) (accessed on 7 April 2022), [https://1 drv.ms/u/stAmgKYzARBl5 ca3 HNaHll2p_Dkjs](https://1 drv.ms/u/stAmgKYzARBl5 ca3 HNaHll2p_Dkjs) (accessed on 7 April 2022).  
Conflicts of Interest: The authors declare no conflict of interest.