# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 5 Conclusions  
This paper mainly studies the three-channel remote sensing cloud detection method based on the fusion of multi-view information at the super pixel level. Firstly, the segmented super pixels are used to establish a super pixel remote sensing image database. Secondly, a variety of feature extraction mechanisms are used to extract three view features of super pixel blocks containing privileged information views. Finally, an SVM classifier that can utilize privileged information features is constructed, and a solution strategy based on quadratic convex optimization is proposed. The classifier is used to judge the super pixel category organization one by one to generate the cloud mask. Experiments are carried out on GF1_WHU and Cloud-38 datasets with different cloud content data. From the results of qualitative and quantitative analysis, we can see that the proposed method has good performance, and it also has good detection effect in scenarios with large differences in cloud distribution and cloud content. In the future, we consider improving the model by using transfer learning technology to make the model quickly adapt to the cloud recognition of multi-style remote sensing images. The algorithm proposed in this paper is based on the improvement of SVM binary classifiers. It is necessary to study a new strategy for multi-classification tasks such as accurate cloud classification, which is also a direction worthy of study in the future.  
Conceptualization, Q.H. and Y.X.; methodology, Q.H. and W.Z.; software, Q.H.; validation, Q.H., Y.X. and W.Z.; writing--original draft preparation, Q.H.; writing--review and editing, Q.H., Y.X. and W.Z.; visualization, Q.H.; supervision, Y.X.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tianjin \"Project + Team\" Key Training Project grant number XC202022 and Tianjin Research Innovation Project for Postgraduate Students grant number 2021 YSS095.  
Not applicable.  
The authors declare no conflicts of interest.  
The following is a description of the abbreviations used in this paper.  
\begin{tabular}{l l l} SVM & Support vector machine \\ MKL & Multi-kernel learning \\ SMO & Sequential minimum optimization \\ MKNPSVM & Multi-kernel framework with nonparallel support vector machine \\ KCCA & Kernel canonical correlation analysis \\ FCN & Full convolution networks \\ MCPK & Multi-view learning coupling privileged kernel method \\ CNN & Convolutional neural network \\ CSPFE-Net & Cloud super pixel feature extraction network \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Jaccard Index** & **Pression** & **Recall** & **Specificity** &
\begin{tabular}{c} **Overall** \\ **Accuracy** & **F1-Score** \\ \end{tabular} \\ \hline Cloud-Net [15] & 87.13 \(\pm\) 5.14 & 91.22 \(\pm\) 4.37 & 95.94 \(\pm\) 3.89 & 89.03 \(\pm\) 4.34 & 93.14 \(\pm\) 5.36 & 92.87 \(\pm\) 4.68 \\ HFCNN [19] & 92.04 \(\pm\) 3.69 & 92.95 \(\pm\) 3.98 & 98.25 \(\pm\) 5.01 & 92.24 \(\pm\) 4.36 & 95.86 \(\pm\) 5.23 & 94.21 \(\pm\) 3.67 \\ SVM & 79.02 \(\pm\) 4.37 & 82.83 \(\pm\) 5.04 & 92.87 \(\pm\) 4.76 & 82.78 \(\pm\) 3.85 & 86.74 \(\pm\) 4.19 & 87.86 \(\pm\) 3.92 \\ MCPK & 87.45 \(\pm\) 3.94 & 92.32 \(\pm\) 4.18 & 96.83 \(\pm\) 5.03 & 88.34 \(\pm\) 4.76 & 92.89 \(\pm\) 5.14 & 94.03 \(\pm\) 3.87 \\ Our method & **94.87 \(\pm\) 1.24** & **95.28 \(\pm\) 1.39** & **98.74 \(\pm\) 2.07** & **93.48 \(\pm\) 2.11** & **97.28 \(\pm\) 1.47** & **97.37 \(\pm\) 1.86** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance on Cloud-38 test dataset (mean \(\pm\) variance (%)).