# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 1 Introduction  
Clouds are aerosols consisting of large amounts of frozen crystals, minute liquid droplets, or particles suspended in the atmosphere ([[https://www.weather.gov/](https://www.weather.gov/)]([https://www.weather.gov/](https://www.weather.gov/))). Their size, type, composition and movement reflect the atmospheric motion. Especially the cloud type, as one of crucial cloud macroscopic parameters in the cloud observation, plays a vital role in the weather prediction and climate change research [1]. Currently, a large quantity of labor and material resources are consumed because ground-based cloud images are classified by qualified professionals. Therefore, developing automatic techniques for ground-based cloud recognition is vital. To date, there are various devices for digitizing ground-based clouds, for example the whole sky imager (WSI) [2], the infrared cloud imager (ICI) [3], and the whole-sky infrared cloud-measuring system (WSIRCMS) [4] etc. With the help of these devices, various methods for automatic ground-based cloud recognition [5; 6; 7] have been proposed. However, the cloud features used in these methods are not discriminative enough to represent cloud images. Practically, the appearance of clouds can be regarded as a type of natural texture [8]. Hence making it reasonable to use texture descriptors to portray cloud appearances. Inspired by the success oflocal features in the texture recognition field [9; 10; 11; 12], some local features are proposed to recognize ground-based cloud images [13; 14]. This kind of method includes two procedures; initially, the cloud image is described as a feature vector using local features. Secondly, the Euclidean distance or chi-square distance is utilized in the matching or recognizing process. The major focal point of the existing methods is based on recognizing cloud images which originate from similar views. These methods are implemented under the condition that the training and test images come from the same feature space. Nevertheless, these methods are not suitable for multi-view cases. This is because the cloud images captured from different views belong to different feature spaces. Practically, we often handle cloud images in two views. For instance, the cloud images collected by a variety of weather stations possess variances in image resolutions, illuminations, camera settings, occlusions and so on. This kind of cloud images actually distributes in different feature spaces. As illustrated in Figure 1a, the cloud images are captured in multiple views, and vary greatly in appearance. The competitive methods for ground-based cloud recognition, i.e., local binary patterns (LBP) [15], the bag-of-words (BoW) model [16], and the convolutional neural network (CNN) [17], generally achieve promising results when training and testing in the same feature space, while the performances degrade significantly when training and testing in different feature spaces, as shown in Figure 1b. Therefore, we hope to employ cloud images from one view (feature space) to train a classifier, which is then used to recognize cloud images from other views (feature spaces). This is a kind of view shift problem, and we define it as the multi-view ground-based cloud recognition. It is very common worldwide. For instance, for the sake of obtaining completed weather information, it is essential to set up more new weather stations to capture cloud images. However, due to the fact that there are insufficient labelled cloud images in the new weather stations to train a robust classifier makes it unrealistic to expect users to label the cloud images for new weather stations. This is time-consuming and a dissipate of manpower. Considering that there are many labelled cloud images accumulated in the established weather stations, we aspire to employ such labelled cloud images to train a classifier which can be used to recognize cloud images in new weather stations. In this paper, we propose a novel multi-view ground-based cloud recognition method by transferring deep visual information.