Debris-flow is one of the major geological hazards in southwest China, which are a global threat and happen and results in thousands deaths and injuries and billions of dollars in damages globally. Automatic multi-temporal DEM coregistration for detecting terrain changes is an attractive but inherent very difficult research topic. Many methods have been proposed in recent years, but all of them can only deal with DEM with limited percentage of terrain changes. However, in landslide and debris-flow areas, the rate of terrain changes is very high. To solve such a problem, a new method for detecting terrain changes using local invariant patches is proposed in this paper. According to the character of the debris-flow activities, the peak and ridge are rarely affected. From where some invariant patches can be extracted associated by the feature extraction method. After co-registration these invariant patches, a coarse matching can be reached. Therefore, two DEM can be compared after applying this coarse matching. With an appropriate threshold, most of terrain changes can be eliminated, and then a fine matching can be anticipated reasonable. The accuracy terrain changes will be derived. The new method can estimate the terrain changes quantificationally and automatically, and verifies by a real application. The experimental results illustrate the proposed method is of robust, accuracy, and timeefficiency.

Digital elevation model (DEM) matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator, in which terrain changes are treated as gross errors. However, all existing methods only emphasise their deformation detecting ability, and neglect another important aspect: only when the gross error can be detected and located, can this system be useful. This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem. The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors. Therefore, treating the terrain changes as gross errors is theoretically feasible, allowing real DEM deformations to be detected by employing a surface matching technique.

The deformation detection method without control points is one of the key techniques for multi-temporal digital elevation model (DEM) analyses, and represents an attractive and difficult research topic. A novel method for improved DEM deformation detection is proposed in this paper, based on the Least Z-Difference algorithm with differential model (DM-LZD). In the new method, an additional parameter is employed to improve the weighting function for the observations in the matching algorithm. Three indexes are designed to give an in-depth and quantitative analysis of the performance, according to the possible two types of errors occurring in the weighting function. The experimental result, based on the simulated dataset, shows that with an appropriate additional parameter it will achieve a better balance between the deformation-detecting ability and the matchingaccuracy, and so generates better performance.

In this paper, a new filtering algorithm of LiDAR point clouds is presented, which can work well in complex cityscapes. This method generates marker image from grid DSM by erosion firstly, and then geodesic dilate on marker image repeatedly to implement opening by reconstruction process, finally white top-hat reconstruction are used to achieve the nDSM and classify ground and non-ground object points. When tested against the ISPRS LiDAR reference urban dataset and compared with representative filtering methods, this approach is particularly effective at minimizing Type I error and Total error rates, while maintaining acceptable Type II error rates. So this approach has good reliability and practicability in complex urban area.