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When point cloud datasets are collected,a number of component values are collected for each point as well.This can include information like color,coordinates,elevation,intensity,and classification.Although point clouds contain a large amount of information,sometimes there is no need to have it all.Having to process a large dataset just to analyze a single type of feature within it is inefficient and time consuming.ThePointCloudSplittertransformer is useful for dissociating the features of interest in the point cloud from the rest of the points.
The examples below demonstrate how the PointCloudSplitter can be used in different scenarios.To learn more about the PointCloudSplitter,visit thedocumentation page.
When point cloud data is collected,a laser point is sent towards the earth and reflected back towards the sensor.Sometimes there are multiple returns for the one laser pulse that is sent.These pulses are counted and given a return number.More reflective surfaces tend to have more returns than non-reflective surfaces.With the PointCloudSplitter,there are four options for how to split a point cloud by return: return,number_of_returns,First Return Only,or Last Return Only.The steps below demonstrate how to split the points by return:
a.b.
c.d.
Image a.shows original point cloud read as an LAS feature type in the Data Inspector while images b.,c.,and d.show the first,second and third return points respectively.
Splitting a point cloud by class can be very useful if you are interested in a one or many features.The classification values are assigned based on the standard ASPRS classification values.
Since this type of operation is common,a 亚搏在线Safe staff member created a simple custom transformer calledPointCloudLASClassifierto make the operation even more efficient.It is essentially one large TestFilter that is preset to know what values to look for.This means you will not have to manually add and set up Tester or TestFilter transformers as was done in the previous example.The steps to use this transformer are as follows:
a.b.
c.d.
Image a.shows the original point cloud read as an LAS feature type in the Data Inspector while image b.shows the points classified as ground (2),image c.shows the points classified as high vegetation (5),and image d.shows the points classified as buildings (6).
This next example is more complex as we will be using two PointCloudSplitter's and an orthophoto to provide additional colouring to the resulting point cloud.Depending on your needs,you may be interested in only a certain class of points as well as another components like intensity,elevation,colour,etc.Further manipulation of colour can help to distinguish differences or areas of focus.To learn how this can be done,follow the steps below:
a.b.
c.d.
Image a.shows the original point cloud read as an LAS feature type in the Data Inspector while image b.shows the ground classified points with z values ranging from 0-5 which were obtained by using two PointCloudSplitter transformers.Image c.shows what the orthophoto of the same area looks like and image d.shows what the result of colour manipulation looks like on the ground classification points.
To learn more about change point cloud colours,please visit our articleColour and Point Clouds.
Thanks - perfect,
just recently returned to FME (2 years off) and point clouds from airborne lidar (10 years off :) and needed this one.Combining with PointCloudComponentAdder (adding colours) and Imagerasterizer to visualize extent of 2 distinct pointclasses in a dataset from Optech Titan scanner (ground and riverbed).Very handy.
pH
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