# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 1 Introduction  
Recently, space technology has been developing continuously [1]. The performance of remote-sensing satellites has been greatly improved and the remote-sensing data service system based on spatial information has been formed [2]. However, according to statistics, more than 65% of the Earth's surface is covered by clouds [3], most of the optical remote-sensing data are affected, and its analysis value is greatly reduced. As the \"first step\" of remote-sensing image processing tasks [4], cloud detection is an important means to assist researchers in evaluating the quality of remote-sensing data and in speeding up remote-sensing data processing. In addition, the cloud detection task is more challenging because of the diversity of the cloud layer itself and the complexity of the background in the remote-sensing data. Therefore, it is of great significance to study high-precision and high-university cloud detection methods. At present, the research on cloud detection of optical remote-sensing image data is being widely carried out, and a large number of methods have emerged [5]. There are three main types of cloud detection methods: (1) traditional image processing methods based on thresholding and texture analysis, (2) statistical learning methods based on hand-designed features of physical attributes [6], and (3) deep learning methods based on deep convolutional neural networks (CNN). FMask [7; 8; 9] is a well-known cloud detection method based on thresholds. This method supports multiple Landsat series satellites and has been applied for some scenarios. However, taking the Fmask3.2 algorithm as an example, it is necessary to use the atmospheric apparent reflectance image of the multispectral band as the main input, and at the same time use the atmospheric reflectance image of the cirrus bands as the auxiliaryinputs to get good results. This limits the university of the FMask method. Although statistical-based processing methods have been designed on the basis of learning, the feature extraction methods of the models are still mainly designed manually. For example, [PERSON] et al. propose a model that uses multiple spectra to extract temporal and spatial features and put the features of a Bayesian probability model for classification. Handcrafted traditional image processing methods mostly require manual designs of parameters such as thresholds, which will cause the quality of the model to be highly dependent on the designer's professional knowledge and professional experience. When the model requires multiple spectral band data, this will further reduce the versatility of the model. Cloud detection methods based on convolutional neural networks (CNN) [10] have achieved advanced results for a variety of scenes [11], and the idea of semantic segmentation of images by pixel classification has joined the current mainstream. The CNN-based method has made important breakthroughs in cloud detection with its excellent local feature extraction capabilities. For example, Cloud-Net+ [12] is a fully convolutional neural network model that focuses on using Landsat8 satellite data. It achieved state-of-the-art (SOTA) on the 38-Cloud public dataset. The deep learning method has the ability of automatic feature extraction and automatic learning, which can still guarantee good results when only a small amount of spectral band data is used as input. Consequently, the cloud detection method based on deep learning has strong versatility [13]. However, these methods still have some limitations. The receptive field of CNN is limited by the size of the convolutional kernel, leading to the shortcomings of CNN-based methods in extracting global features. With the development of computer vision technology, the Transformer [14] network, which itself is used in the direction of natural language processing, began to be introduced into the field of computer vision and demonstrated excellent performance. By dividing the image of multiple patches, the image can be smoothly sent to the Transformer.