# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 4 Experiments
### Influence of Parameter Variances  
In this section, we analyze the proposed TDLBP + WML in three aspects, including the selection of the convolutional layers for PSMs, and the influences of \(K\) and \(r\). It is noted that we select the IAP_e as one view, and the MOC_e as the other view to implement the following experiments. Generally, we can extract structural and textural local features from shallow convolutional layers of a CNN, and extract features with high-level semantic information from deep convolutional layers. The appearance of clouds can be regarded as a type of natural texture, and therefore we extract feature from the PSMs in the shallow convolutional layers. We select the first to eighth convolutional layers for the PSMs to analyze the performance of TDLBP + WML. From Table 8, it is obvious that the highest result of TDLBP + WML is obtained when we make use of the PSMs in the 4-th convolutional layer. Since each convolutional layer contains different information, we extract TDLBP features from two different convolutional layers for cloud image representation in order to obtain the completed cloud information. From Table 9, TDLBP + WML obtains the highest result when utilizing the PSMs of _conv_4_, and therefore we combine _conv_4_ with each of the other convolutional layers for TDLBP feature extraction. Specifically, we extract TDLBP features from this kind of two convolutional layer, and the resulting TDLBP features are concatenated to form the final feature for describing the cloud image. Comparing Table 9 with Table 8, the performances all improve, and the case of _conv_3_ & _conv_4_ achieves the best result of 79.46%. Based on this result, we further combine TDLBP features for three different convolutional layers, and follow the same procedure of feature extraction as mentioned above. The results are shown in Table 10. Comparing Table 10 with Table 9, the performances slightly degrade. Hence, considering both the computation complexity and the recognition accuracy, we conclude that extracting TDLBP features from two different convolutional layers is optimal for cloud image representation. The effect of \(K\) on recognition performance is shown in Figure 6, and \(K\) is the number of PSMs for the 4-th convolutional layer. We can conclude that larger \(K\) may result in better recognition accuracies, \begin{table}
\begin{tabular}{l l l l l l} \hline
**One View** & **The Other View** & **LBP** & **BoW** & **CNN** & **TDLBP + WML** \\ \hline MOC\_e & CAMS\_e & 31.38 & 38.91 & 54.47 & **71.87** \\ MOC\_e & IAP\_e & 41.24 & 43.85 & 58.82 & **76.91** \\ CAMS\_e & IAP\_e & 32.54 & 41.26 & 56.18 & **73.58** \\ CAMS\_e & MOC\_e & 39.17 & 42.57 & 56.87 & **74.65** \\ IAP\_e & CAMS\_e & 33.86 & 40.25 & 54.35 & **72.84** \\ IAP\_e & MOC\_e & 42.95 & 45.86 & 61.26 & **78.52** \\ \hline \end{tabular}
\end{table} 
Table 7: Multi-view cloud recognition accuracies (%) comparing TDLBP + WML with three representative methods, i.e., LBP, BoW, and CNN. \begin{table}
\begin{tabular}{l l} \hline \hline
**Layer** & **TDLBP + WML** \\ \hline conv\_1 & 65.13 \\ conv\_2 & 67.68 \\ conv\_3 & 75.39 \\ conv\_4 & **78.52** \\ conv\_5 & 74.81 \\ conv\_6 & 68.23 \\ conv\_7 & 65.74 \\ conv\_8 & 64.85 \\ \hline \end{tabular}
\end{table} 
Table 8: The performance of TDLBP + WML in different convolutional layers. but may probably lead to heavier computational burden. We obtain the best result when \(K\) increases to 8. \(r\) is the the number of eigenvalues (see Section 3.3). \(r\) has an impact on recognition performance as it controls the dimensionality of \(M\). In addition, we evaluate the performance of TDLBP + WML with respect to \(r\). As illustrated in Figure 7, with \(r\) increasing, the recognition performance improves, and the best result of 78.52% is obtained at a certain point where \(r\) is equal to 150. The proper \(r\) can make the feature vectors contain the discriminative information with the favourable dimensionality.