# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 3 Proposed Method  
### Multi-View Feature Extraction  
View A: Texture, boundary, and other features are extracted from the super pixel circumscribed rectangular region through a multi-layer joint convolutional neural network (CNN). Cloud area has similar shape to super pixel boundary, which is quite different from grassland area and water area boundary. In this paper, we use the inherent multi-scale pyramid level of a deep convolution network to develop a top architecture with horizontal connection, which is used to construct high-level feature maps at all scales. Features can be easily extracted by multi-scale feature extraction structure in the superpixel circumscribed rectangle, and texture, boundary, gradient, and other information can be obtained from multiple horizons. The feature extraction layer Conv3 in the deepest part will extract more abstract features. View A feature extraction is realized by cloud super pixel feature extraction network (CSPFE-Net); CSPFE-Net structure is shown in Figure 1. In the network structure, the super pixel external rectangular region has the input of \(3\times 16\times 16\) images. Conv refers to the convolution layer. Relu increases the nonlinear ability. Pooling downsampling is used to further extract the image. LRN is normalized to constrain the data within a certain range. Concat refers to stitching vectors. The features extracted from each feature extraction layer are weighted splicing, and are jointly output as the feature vector through the full connection layer. The feature vector with the super pixel output structure of \(1\times 64\) under the fine scale is sufficient to complete the feature representation.  
View B: The color statistical characteristics extracted by the super pixel itself, excluding the black edge region of the circumscribed rectangle. Specifically, we calculate the mean, variance, maximum, minimum, and median of the data describing the color in each channel. \(SP_{i}\) represents three-channel data for the i-th super pixel, and RGB color statistics can be calculated by Equation (3). The feature vector format of view b is \(1\times 15\).  
\[getCF(x)=[mean(x),std(x),max(x),min(x),median(x)] \tag{2}\]  
\[RGBfeature=[getCF(SP_{i}(R)),getCF(SP_{i}(G)),getCF(SP_{i}(B))] \tag{3}\]  
where \(getCF(x)\) extracts and splices the statistical features of the data of one channel \(x\); \(SP_{i}(R)\), \(SP_{i}(G)\), and \(SP_{i}(B)\) represent three visible light channels: red, green, and blue.  
View P: View P (privileged information view) is the feature of privileged information. The correction space guided by privilege information can correct the classification plane of multi-view, which can make the performance of multi-view classifier better. The feature of cloud-containing super pixel privileged information includes two parts. The first part is the specific cloud content in the super pixel. The second part is the label of the fast adjacent block of the super pixel. Generally, two super pixel blocks with the nearest center distance are selected, and this part is the feature vector of \(1\times 3\).  
Figure 1: CSPFE-NET structure.