###### Abstract
In the proposed scheme, cloud cover information in front of the satellite is obtained with the forward-looking cloud detector, and then a reactive/deliberative framework can be achieved through an onboard autonomous decision. [PERSON] et al. [11] considered an agile satellite with flexible attitude can avoid clouds during imaging using a synchronous cloud detector, and proposed a hierarchical scheduling method for the real-time scheduling problem. The method divided the scheduling process into three steps: pre-assignment, rough scheduling, and fine scheduling, which can compose a hierarchical scheduling algorithm based on ant colony algorithm. [PERSON], et al. [12] considered high resolution satellites operating on the same orbit, which had wide-swath images and providing the cloud cover information close in time. Then, the model of avoiding clouds under satellite orbital space coordinate system were established to guide the online mission planning of the high spatial resolution satellite. [PERSON] et al. [13] studied a machine learning method for onboard cloud detection and used real-time cloud cover information to guide the autonomous planning of the satellite. However, the limitation of the methods described above are that the optical satellites require incorporating a synchronous payload to obtain cloud cover information, which lacks universality in practical applications. Thirdly, during the ground data processing period of LEO satellite, cloud detection [14] provides the cloud mask firstly, and then images with much cloud would be discarded. Also, enhancement of optically thin cloud scenes [15], and restoration of thick cloud scenes [16] can be used to recover image information from cloud cover scenes. The main methods of cloud detection include threshold segmentation, texture analysis, change detection, and integrated detection. The threshold segmentation method is based on the difference between cloud and surface brightness values, and cloud area extraction is achieved by threshold segmentation. The selection of the threshold value is very critical, and the fixed threshold value is only applicable to specific image scenes, from which the threshold segmentation method has been gradually developed into dynamic threshold segmentation [17], and multi-band combined threshold segmentation [18], etc. The threshold segmentation method has the advantages of simplicity and ease of implementation, but it is prone to misjudgment for high brightness cloud-like surface targets (e.g., ice, snow, etc.). The texture analysis method identifies clouds based on the similarity within similar features and the discontinuity of boundaries between different features, such as the fractal dimension method [19] and the gray level co-occurrence matrix method [20]. The detection accuracy using only texture features is limited due to the large variation of cloud features. The change detection method uses clouds as a change target in the image and uses two or more images with similar temporal phases of the same area [21], which has the advantage of high detection accuracy and the disadvantage of requiring images in the same regions, short temporal phases, and strict geometric consistency. Integrated detection methods combine radiometric, geometric, and temporal features for cloud detection, such as support vector machines [22], neural networks [23], and object-oriented image analysis [24]. With the development of deep learning and its successful application in the field of computer vision, cloud detection based on deep learning has attracted wide attention. The main methods include full convolutional networks [25], multi-scale convolutional feature fusion [26], super-pixel segmentation combined with convolutional neural networks [27]. Ground data processing methods can improve the utilization of cloud cover images to a certain degree, but they essentially belong to the data processing after mission planning, which is also wasting resources of imaging and data downlink.