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| layout | title | description | img | importance | category |
|---|---|---|---|---|---|
| page | Spectra | Application using the morphological hierarchies and LiDAR data | /assets/img/spectra.png | 1 | thesis |
This application allows interactive filtering of LiDAR data. We can retrieve structures in the LiDAR data based on the elevation values of the connected components and their shape and size attributes. To guide the attribute thresholding we can plot the attribute space representing all the shapes and sizes of the structures contained in the LiDAR data. Such a plot can be called a shape-size pattern spectrum.
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We can use this spectrum to select the attribute thresholds. The current application allows us to do this in real time!
In this spectrum, the x-axis represents the area attribute. Selecting the right part of the spectrum allows us to filter the largest connected components, while selecting the left part allows us to filter the smallest connected components. In between we can characterize different classes of structures sizes, including cars, trees, buildings. The y-axis represents the compactness attribute. The compactness attribute is defined as the ratio between the area of the connected component and the square of its perimeter. It ranges from 0 for non-compact shapes to 1 for compact shapes. The top of the spectrum represents compact shapes (e.g. circular shapes) while the bottom of the spectrum represents linear shapes.
Selecting thresholds in the attribute space can still be difficult. We propose to drive the threshold selection by the example. The application allows to select in the LiDAR data structures and to highlight their shapes and sizes in the spectrum.
In the previous example we showed the use of two attributes, area and compactness. However, there are many more that we can use or even define, depending on the purpose of the application.
Furthermore, the attribute space does not necessarily have to be limited by two dimensions. If for now we are limited to a maximum of 3 dimensions for understanding and visualisation (as seen in the 3D spectrum bellow), we can look forward to using new visualisation and innovative user interfaces to jump into the multi-dimensional attribute space exploration!
The underlying data structure for processing LiDAR data are hierarchical morphologies, in particular component trees, which allow an efficient representation of nested connected components for the computation of shape and size attributes as well as efficient filtering.
The application was developed in Python using the SAP package to build the trees, compute the spectra and filter the data.
This application was developed as part of my Ph.D. thesis.