To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

The Internet Engineering Task Force (IETF) proposed the Session Initiation Protocol (SIP) as the IP-based telephony protocol. With the widespread application of the Voice over IP (VoIP) on Internet, Security problems of SIP have received a lot of attention of researchers and the authentication mechanism in SIP is becoming increasingly important. Many studies reveal that the initial version of SIP specification suffers malicious attacks. To improve the security of the authentication mechanism in SIP, amendments and supplements are expressed over years. Researches on the previous specification have been carried out and some attacks are found, such as replay attack, online dictionary attack, man in the middle attack, etc. However, there is currently a lack of security analysis to the fresh security mechanisms of SIP. In this paper, we accommodate such a requirement by analyzing the security properties of SIP Digest Access Authentication Scheme adopting a formal protocol analysis tool SPAN. The authentication scheme is modeled in the validator according to two practical scenarios. With the two back-ends of SPAN, the two models of authentication scheme are verified both as safe. This result confirms that the supplemented version of SIP authentication mechanism is more reliable.

Accurate land information using was the foundation of the modern agriculture. The Sential-2 images were used as data sources in this paper. Firstly, spectral curve analysis of the features in the study area was performed and non-vegetated areas were excluded by using decision tree method. Since the same texture feature in each band had a certain autocorrelation, the principal component analysis method was used to reduce the dimension. In the end, 49 features including original band, vegetated band, texture feature could be achieved, then 3 models with different input which were classified by random forest method could be set up. The result showed that the model which used original band, vegetation index and decreased dimension texture feature had the highest accuracy. The total classification accuracy and Kappa index was 91.03% and 0.81771 respectively. Therefore, some conclusions were figured out: (1) the classification method based on multiple features was helpful to improve the accuracy; (2) the time series characteristics of vegetation index could help improve the classification accuracy of non-main crops; (3) decreasing the dimension of texture feature could improve the efficiency and cartographic quality of the classification.

After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non‐feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three‐dimensional modeling.