Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.
Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.
With the continuous development of Airborne Lidar hardware, the current data collection system will not only collect information on a single echo, multiple echo information also can be available. Through the analysis and discussion of echo principle, this paper compares and elaborates the characteristics of single-echo and multiple echo information, and introduces a filter classification method based on echo information, and illustrates that the method is simple and effective according to an example.
Accurately estimating the deformation of tunnel surrounding rock is a very important work for surveyors, and we adopted grey model as a forecasting means because of its fast calculation with as few as four data inputs needed, however, the original GM (1, 1) model is not fit for dynamic and long data prediction. For this purpose, we propose a novel approach to improve prediction accuracy of GM(1,1) model through optimization of the initial condition and adoption the technique of rolling modeling, the new forecasting model termed RnGM(1,1). By using the optimized model to analyze and predict the deformation of tunnel surrounding rock and comparing this optimized model with other models, we finally draw a conclusion that this optimized model is able to improve the precision of prediction and therefore can be applied to deformation data analysis.
With double-temporal Landsat TM and ETM+ datasets, the change information of forest resources of Culai Mountain in Shandong Province was explored. This paper applies decision tree classification based on C5.0 algorithm and neighborhood correlation image analysis to detect forest change information,and compares the three different detection methods:1)C5.0 classifies single-temporal data respectively,and extract change information after comparing classification results;2) create C5.0 train rules through double-temporal raw data,then generate change detection map;3)In addition to double-temporal remote sensing data,neighborhood correlation analysis images are also added as one of the data sources of C5.0,and generate change detection map. The experimental result shows that decision tree classification based on C5.0 algorithm can detect change information effectively,and after adding neighborhood correlation analysis images the classification accuracy of change detection was improved.
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