# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 1 Introduction  
Remote sensing images are widely used in land resource utilization, meteorological monitoring, and geology [1; 2]; however, the monitoring data of sensors are often affected by clouds. Many remote sensing studies have been plagued by cloud occlusion, resulting in inaccurate observation results [3]. Therefore, it is necessary to accurately detect the cloud in remote sensing images. Most cloud detection methods are highly dependent on available spectral bands, and use specific physical constraints to separate different categories according to spectral bands [4]. In particular, when relying on handmade features or experiences and searching for specific thresholds, these methods will have some problems in the segmentation of categories, and there may be some situations that cannot be segmented from the spectral threshold, such as deserts and super-high-brightness pixel areas [5]. The key of the threshold-based method is how to choose the best threshold to distinguish foreground cloud and background surface. Early fixed threshold methods failed to meet the increasing accuracy requirements. Therefore, more and more dynamic adaptive threshold methods are proposed for the difference between cloud features and surface features. [PERSON] et al. [6] used two images with different channels to incorporate the spatiotemporal change threshold into the cloud detection process. [PERSON] et al. [7] proposed an automatic cloud detection algorithm for remote sensing image observation statistics. They improved the global threshold method and gradually improved the detection results. With the development of machine learning, the method based on Markov random field [8] and the widely used SVM [9; 10; 11; 12] are becoming more and more popular in cloud detection. There are also some deep networks used as tools for cloud detection tasks. For example, [PERSON] et al. [13] proposed a hybrid full convolution network (FCN) and gradient recognition algorithm and applied FCN to the field of cloud detection. [PERSON] et al. [14] proposed a framework that combines convolutional neural networks, adapted to the cloud recognition task through a transfer learning approach, using voting rules. The cloud-net algorithm has better detection effect by redesigning the convolution block based on FCN [15], and usually uses the cloud network as the baseline of the deep learning cloud detection network. The multilevel feature fused segmentation network (MFFSNet) algorithm uses a pyramid pooling module to aggregate feature information at different scales to improve the utilization of local and global features of clouds in images [16]. In the detection of cloud area in cloud-containing remote sensing images, judging from the pixel level or dividing the image into rectangular blocks, super-pixel-level judgment is more effective than pixel-level judgment [17; 18]. [PERSON] et al. [19] proposed a cloud judgment method combined with several statistical characteristics of super pixels and conducted experiments. The above studies show that judging cloud or non-cloud with super pixel as the basic unit can obtain excellent cloud and non-cloud segmentation results of remote sensing images. Many methods determine labels according to the number of cloud-containing pixels or the proportion of cloud-containing pixels in the super pixel, and cannot make good use of the label data of adjacent pixels and their own cloud proportion data. The information between super pixels cannot be well combined. The current methods are difficult to define the super pixel as a certain category, or the super pixel cannot fully contain the cloud, and the corresponding cloud-containing state information cannot be reasonably used. At the same time, most remote sensing images now use multiple sensors to collect data, but there will be only three RGB visible light channels of data. For this image, a segmentation method with high accuracy is also needed.