# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 1 Introduction
For the remote sensing field, [PERSON] et al. [19] first proposed a zero-shot remote sensing scene classification (RSSC) method called ZSL-LP. ZSL-LP constructs a semantic-directed graph, then uses a label-propagation algorithm for zero-shot classification. [PERSON] et al. [20] introduced a novel zero-shot RSSC method, which relies on Sammon embedding and spectral clustering. They modified semantic feature space class prototypes by Sammon embedding, which ensures the consistency with the visual feature space class prototypes. [PERSON] et al. [21] introduced a distance-constrained semantic autoencoder for zero-shot remote sensing scene classification. [PERSON] et al. [22] conducted a zero-shot study for fine-grained remote sensing image recognition. They first learned a compatible function and then showed how to transfer knowledge for unseen classes. Although these approaches have achieved promising results, most are devoted to designing visual-semantic embedding models only with seen classes, in which it is difficult to guarantee an excellent extension to unseen classes. In addition, these models trained only with seen data tend to misclassify unseen test instances into the seen category, generating a special imbalanced classification problem. Generative adversarial networks can alleviate the above issues to some extent [14; 17; 23]. As far as we know, no one has tried to apply generative adversarial networks for zero-shot RSSC. The GAN-based method designed for ordinary images cannot apply to remote sensing images well because remote sensing image scenes are complex. Remote sensing images generated by the GAN model may have instability and mode collapse problems. In addition, remote sensing images have inter-class similarity and intra-class diversity. Overall, zero-shot RSSC deserves more exploration. We propose a novel approach for zero-shot RSSC with the mentioned considerations. 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 semantic information we need. Word2 vec [24], Glove [25], Fasttext [26], and Bert [27] are the four natural language processing models. Because of the complexity of remote sensing scenes, we employed the conditional Wasserstein generative adversarial network [28] for generating image features directly instead of images to avoid instability and mode collapse problems in training. We trained a generator that can generate an arbitrary number of class image features from class semantic information, converting the zero-shot classification 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 to ensure the generator can generate image features close to the real image with the class semantic information. Our model is referred to as CSPWGAN. The classification loss is used to preserve inter-class discrimination. We used a semantic regression module to ensure that 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. The contributions of this study are as follows:  
1. We trained a generator that can generate class image features close to the real image through class semantic information. We propose well-designed modules to constrain the generator, including classification loss module, class-prototype loss module, and semantic regression module. To the best of our knowledge, we are the first to employ generative adversarial networks for zero-shot remote sensing scene classification (RSSC);
2. We explored the effect of different semantic embeddings for zero-shot RSSC.