# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 1 Introduction  
Over the past decades, the epoch of remote sensing big data arrived, and remote sensing technology has made excellent progress [1]. It has played an important role in environmental monitoring [2], urban construction planning [3], and land use classification [4]. Currently, the accuracy of remote sensing image classification is very high. Most of the current remote sensing scene classification approaches rely on supervised learning [4, 5, 6], which can obtain excellent results when trained on large-scale datasets [7]. Usually, it requires labeling a lot of image data for each remote sensing image class. When we classify an image, the classifier does not work well if the category corresponding to the image is not in the training set, which is referred to as a zero-shot problem [8]. With the explosive growth of remote sensing image classes, we will encounter many new classes, and it is unrealistic to collect sufficient image samples for each class. It makes sense to identify the unseen class images when the training set does not contain the corresponding class, for instance, a rare aircraft species. In order to overcome these problems in remote sensing images, it is of great interest to study zero-shot learning methods for RSSC. Zero-shot classification methods for remote sensing images are designed to address the classification for unseen category images,and more and more researchers are focusing on this topic. This can alleviate manual labeling, reduce a lot of labor, and make the existing classification model more scalable. Humans can recognize about 30,000 object categories of information [8], and humans can transfer their knowledge to identify new classes when only textual descriptions of the new classes are available. The key of zero-shot learning to identify unseen classes is that zero-shot learning uses semantic and visual information from all image classes. 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. The concept of zero-shot learning can be traced back to 2008 when [PERSON] first introduced zero-shot learning [8]. [PERSON] et al. [9] first extended this approach to the field of computer vision. We can summarize the ZSL methods into two types: The first is embedding-based methods that map image features and semantic embeddings into one space and perform metric learning for ZSL. [PERSON] et al. [10] solved the zero-shot learning task by using the semantic similarity embedding (SSE) method. SSE fuses multiple semantic information into the same semantic space and calculates the similarity of them. [PERSON] et al. [11] used a novel visual-semantic embedding approach to use annotated image data and semantic information from the unannotated text to recognize visual objects. [PERSON] et al. [12] presented a dual visual-semantic mapping framework for zero-shot classification (DMaP), which studies the relationship between the semantic space manifold and the transferability of visual-semantic mapping. However, they cannot work well in the generalized zero-shot learning (GZSL) setting because they overfit to seen classes. The second is GAN-based methods which generate fake image features for unseen classes and train KNN [13] or softmax classifier to perform ZSL. [PERSON] et al. [14] first used generative adversarial networks (GANs) [15] to convert semantic features to visual features, providing a new idea for zero-shot learning. [PERSON] et al. [16] mainly added a cycle consistency loss term, allowing GAN to generate more realistic images. [PERSON] et al. [17] introduced soul samples for generative zero-shot learning and used the cascade classifier to classify the unseen class. [PERSON] et al. [18] presented an episode-based training pattern to improve the model's performance for zero-shot learning. Generative adversarial networks (GANs) [15] is a popular zero-shot classification method. GAN-based methods generally work better than embedding-based methods.