96 lines
4.0 KiB
Markdown
96 lines
4.0 KiB
Markdown
---
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layout: page
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title: Spectra
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description: Application using the morphological hierarchies and LiDAR data.
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img: /assets/img/spectra-480.webp
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importance: 1
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category: thesis
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---
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This application allows interactive filtering of LiDAR data. We can
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retrieve structures in the LiDAR data based on the elevation values of
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the connected components and their shape and size attributes. To guide
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the attribute thresholding we can plot the attribute space representing
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all the shapes and sizes of the structures contained in the LiDAR data.
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Such a plot can be called a shape-size pattern spectrum.
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{.figure-img .img-fluid .rounded
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.z-depth-1}
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## Interactive filtering
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We can use this spectrum to select the attribute thresholds. The current
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application allows us to do this in real time!
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{.figure-img
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.img-fluid .rounded .z-depth-1}
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In this spectrum, the x-axis represents the area attribute. Selecting
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the right part of the spectrum allows us to filter the largest connected
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components, while selecting the left part allows us to filter the
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smallest connected components. In between we can characterize different
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classes of structures sizes, including cars, trees, buildings. The
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y-axis represents the compactness attribute. The compactness attribute
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is defined as the ratio between the area of the connected component and
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the square of its perimeter. It ranges from 0 for non-compact shapes to
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1 for compact shapes. The top of the spectrum represents compact shapes
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(e.g. circular shapes) while the bottom of the spectrum represents
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linear shapes.
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## Example driven
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Selecting thresholds in the attribute space can still be difficult. We
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propose to drive the threshold selection by the example. The application
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allows to select in the LiDAR data structures and to highlight their
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shapes and sizes in the spectrum.
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{.figure-img
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.img-fluid .rounded .z-depth-1 }
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## Wait, there's more!
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In the previous example we showed the use of two attributes, area and
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compactness. However, there are many more that we can use or even
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define, depending on the purpose of the application.
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Furthermore, the attribute space does not necessarily have to be limited
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by two dimensions. If for now we are limited to a maximum of 3
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dimensions for understanding and visualisation (as seen in the 3D
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spectrum bellow), we can look forward to using new visualisation and
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innovative user interfaces to jump into the multi-dimensional attribute
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space exploration!
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{.figure-img .img-fluid
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.rounded .z-depth-1 loop=true autoplay=true}
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## Notes
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The underlying data structure for processing LiDAR data are hierarchical
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morphologies, in particular component trees, which allow an efficient
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representation of nested connected components for the computation of
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shape and size attributes as well as efficient filtering.
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The application was developed in Python using the [SAP
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package](/projects/sap/) to build the trees, compute the spectra and
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filter the data.
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This application was developed as part of my PhD thesis. Experiments
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and quantitative results have been published on use cases in a natural
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environment, related to illegal gold panning and dikes detection [^1].
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[^1]: F. Guiotte, G. Etaix, S. Lefèvre, et T. Corpetti, « *Interactive
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Digital Terrain Model Analysis in Attribute Space* », International
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Archives of the Photogrammetry, Remote Sensing and Spatial Information
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Sciences, vol. XLIII-B2-2020, p. 1203‑1209, 2020, doi:
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[10.5194/isprs-archives-XLIII-B2-2020-1203-2020][doi].
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[doi]: https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1203-2020
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