Application of Improved Electromagnetism-like Mechanism Algorithm on Massive Remote Sensing Image Screening  
[PERSON]  
1  
[PERSON]  
2 Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing 100048, China; [EMAIL_ADDRESS] (L.D. )1 College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; [EMAIL_ADDRESS] (Y.F. ); lisu@th. The storage of massive remote sensing data requires huge storage space and high-performance storage devices. Traditional storage architecture makes it difficult to meet the long-term storage and fast access requirements of such large-scale data. For example, in some large remote sensing data centers, in order to store the ever-growing data, a large amount of money needs to be continuously invested in expanding storage equipment. At the same time, data transmission is also faced with difficulties. Transmitting remote sensing data from the collection end to the processing center or the user end requires a high-bandwidth network support, or else it will lead to serious data transmission delays, which affects the timeliness of the data. If the massive remote sensing data are analyzed directly, the consumption of computing resources is huge and the analysis efficiency is extremely low. When classifying large-scale remote sensing images, without data screening, traditional classification algorithms may take hours or even days to complete the processing. Moreover, redundant information and noise in the data will interfere with the training and prediction of the analysis model and reduce the accuracy of the analysis results. In the face of the above challenges, how to quickly and effectively screen out the data with application value from the massive amount of remote sensing data has become a challenging and practical application research direction. With the increasing diversity of remote sensing data sources, the data volume is huge and the spatial distribution is unbalanced. At home and abroad, the retrieval of remote sensing data is carried out based on conditions such as spatial range, data source, imaging time range, and cloud cover. Among them, the retrieval of the spatial range includes retrieval based on administrative regions, custom-defined regions, longitude and latitude, etc. [3]. However, the number of entries in the query results usually reaches hundreds or even thousands. If users want to carefully screen out the ideal results, they need to rely on manual further selection, which is time-consuming and labor-intensive, seriously affecting the efficiency of users in using remote sensing data. In various monitoring and analysis application scenarios, solving the optimal screening problem enables us to obtain key information more quickly. For instance, after regional natural disasters such as earthquakes, floods, and typhoons occur, it is particularly important to quickly obtain the latest phase of remote sensing imagery covering the affected area. These image data can help decision makers identify the extent of damage to the affected area, the destruction of infrastructure, and the evacuation needs of people, thus providing an important basis for effective post-disaster rescue. In the field of agricultural monitoring, quickly screening out the key images reflecting the growth status of crops is of vital importance for promptly grasping the health and growth trends of crops. This allows farmers and agricultural managers to take timely and effective management measures to ensure agricultural production. In addition, this efficient screening method is equally applicable to a variety of other monitoring scenarios, such as environmental monitoring and urban planning. Therefore, how to obtain the minimum amount of data that meets the requirements from a large number of repeatedly covered data is an urgent issue to be addressed in the early stage of data production. This is related to subsequent data processing and analysis.