The mixed noise formed by a large number of spikes， speckles and multi-directional stripe errors in Shuttle Radar Terrain Mission （SRTM） will cause serious interference to the subsequent applications. In order to solve the problem， a Low-Rank Group Sparsity_Total Variation （LRGS_TV） algorithm was proposed. Firstly， the uniqueness of the data in the local range low-rank direction was used to regularize the global multi-directional stripe error structure， and the variational idea was used to perform unidirectional constraints. Secondly， the non-local self-similarity of the weighted kernel norm was used to eliminate the random noise， and the Total Variation （TV） regularity was combined to constrain the data gradient， so as to reduce the difference of local range changes. Finally， the low-rank group sparse model was solved by the alternating direction multiplier optimization to ensure the convergence of model. Quantitative evaluation shows that， compared with four algorithms such as TV， Unidirectional Total Variation （UTV）， Low-Rank-based Single-Image Decomposition （LRSID） and Low-Rank Group Sparsity （LRGS） model， the proposed LRGS_TV has the Peak Signal-to-Noise Ratio （PSNR） of 38.53 dB and the Structural SIMilarity （SSIM） of 0.97， which are both better than the comparison algorithms. At the same time， the slope and aspect results show that after LRGS_TV processing， the subsequent applications of the data can be significantly improved. The experimental results show that， the proposed LRGS_TV can repair the original data better while ensuring that the terrain contour features are basically unchanged， and can provide important support to the reliability improvement and subsequent applications of SRTM.

For mining research teams more rationally, a teacher-student relationship based research team mining algorithm was proposed. First, the BiLSTM-CRF neural network model was used to extract the teacher and classmate named entities from the acknowledgement parts of academic dissertations. Secondly, the guidance and cooperation network between teachers and students was constructed. Thirdly, the Leuven algorithm was improved, and the teacher-student relationship based Leuven algorithm was proposed to mine the research teams. The performance comparison was performed to the label propagation algorithm, the clustering coefficient algorithm and the Leuven algorithm on the datasets such as American College football dataset. Moreover, the operating efficiency of the teacher-student relationship based Leuven algorithm was compared to the operating efficiency of the original Leuven algorithm on three academic dissertation datasets with different scales. Experimental results show that the larger the data size, the more obvious performance improvement of the teacher-student relationship based Leuven algorithm. Finally, based on the academic dissertation dataset of National University of Defense Technology, the performance of the teacher-student relationship based Leuven algorithm was validated. Experimental results show that research teams mined by the proposed algorithm are more reasonable compared to academic paper cooperation network based mining method in the aspects of team cooperation closeness, team scale, team internal relationship and team stability.

Because the algorithm of cloud threshold often generates boundary effect, this paper proposed an improved algorithm based on wavelet transform and homomorphic filter. The image with cloud was decomposed by wavelet transform to find the proper number of demarcation levels. Cloud could be removed by making homomorphic filtering to the higher level coefficients, while giving the lower level detailed coefficients and the approximation coefficients some weight factors respectively. The three parts of coefficients were reconstructed and fused to get processed result. The experimental results indicate that the proposed algorithm can remove the thin cloud cover effectively, maintain the details better and prevent producing the boundaries.