LAUSR

With the advent of the era of network information, the amount of data in network information is getting larger and larger, and the classification of data becomes particularly important. Current semi-supervised multi-map classification methods cannot quickly and accurately perform automatic classification and calculation of information. Therefore, this paper proposes an MR-MGSSL algorithm and applies it to the classification of semi-supervised multi-graph. By determining the basic idea and calculation framework of MR-MGSSL algorithm, the mining of optimal feature subsets in multi-graphs and the multi-graph vectorization performance time are taken as examples, and the proposed algorithm is compared with other semi-supervised multi-graph classification methods. The performance evaluation results show that compared with other classification calculation methods, MR-MGSSL algorithm has the advantages of low sensitivity to feature subgraph and short vectorization time. The method is used to extract and detect clouds in remote sensing images (GF-1 and GF-2).