7. Conclusions and recommendations for future classification of 3D lidar data
7.3. Recommendations
Below is the list of recommendations:
1. Data
a. Using data with high point density, resolution.
b. Selecting appropriate grid cell size and as a result having better results of HJD method.
c. Using reflectance data as the reflectance of various objects are very different from each other and in addition to elevation data, it provides stronger constraints.
2. Improve HJD method so that it performs better for dataset with low point density. In other words, improve HJD to be less dependent on point density.
3. To avoid wrong classification between contact and catenary:
a. One possible strategy is to calculate the 80% percentile elevation of all classified contact wire points. Then reclassify contact wire points higher than 80% percentile elevation as catenary wires.
4. To improve TM results:
a. Rotating the kernel with steps bigger than 1 degree.
b. Improve the kernel so that it represents the configuration of railway environment more accurately and precisely.
c. Implementing in Python instead of in MATLAB.
5. To improve RG results:
a. Customizing the algorithm to conditions of study area. For instance, for a high resolution dataset, small growing steps results in updating height and direction more frequently as well as higher speed of algorithm. So it will work even in steep areas with curved rails and wires. While for a low resolution dataset, growing step should be at least bigger than the biggest gap in the data so that it is able to classify points even after gaps in the data.
6. To avoid wrong classification of points on towers as catenary wires:
a. One option is first to classify the points on towers. Then for classification of points on catenary wires, only classify points which are not classified yet. This would prevent this wrong classification of catenary wires.
7. To improve the rail points’ classification results:
a. Results of 3D rail modelling done by Diaz Benito (To be published in March 2012) can
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