# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 4 Experiment
### Quantitative Analysis  
In the description of the overall results, the Jaccard index is used to describe the similarity between the predicted mask and the real mask, which is widely used in the performance evaluation of cloud detection tasks. Precision is the ratio that predicts the number of true values to the number of cloud tags in cloud data. Recall represents how many clouds can be predicted in all marked cloud data. Specificity index is used to measure the integrity of error prediction, and the overall accuracy index is used to represent the accuracy of the cloud/non-cloud binary classification. F1-score considers the relationship  
Figure 4: The performance of several methods on GF-1_WHU dataset [32] with different cloud distribution styles. Figure 5: The performance of several methods on Cloud-38 dataset [33] with different cloud distribution styles. between precision and recall. The calculation method of each evaluation index is shown as Equations (10)-(15). We divide each data image in GF-1_WHU and Cloud-38 into \(400\times 400\times 3\) specifications, and randomly select 80% of the dataset as the training set image, which is the test set image. Specifically, in the GF-1_WHU dataset, there are 4246 images, including 3369 training images and 850 test images. In the cloud-38 dataset, there are 15,200 images, including 12,160 training images and 3040 test images. Each index data is shown as (mean \(\pm\) variance). \[JaccardIndex=\frac{TP}{TP+FN+FP} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{13}\]  
\[OverallAccuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{15}\]  
where \(TP\) represents the number of positive samples with positive judgment results, \(TN\) represents the number of negative samples with negative judgment results, \(FP\) represents the number of negative samples with positive judgment results, and \(FN\) represents the number of positive samples with negative judgment results. As shown in Tables 2 and 3, our method achieves high scores in both groups of test results. The Cloud-net [15] method needs a certain number of training sets to annotate data, and cannot use the location information of pixel blocks. HFCNN also uses super pixels as the basic unit of cloud detection, but it uses only one feature extraction method, and cannot use the privilege information marked by super pixels, so it performs poorly in some confusing areas. The basis of its use is \(32\times 32\times 3\) and the segmentation effect may be rougher. By comparing the SVM, MCPK, and our methods, it is proved that the multi-view structure has better performance than the method of directly stitching combined features. At the same time, thanks to the feature extraction of super pixel multi-view and the utilization of privilege information, our method is more accurate for the recognition of the cloud area. The experimental results show that no matter whether the type of substrate in the background is more or less, our method can obtain excellent results, and the results of each index prove the feasibility and effectiveness of the proposed remote cloud sensing detection method.