166 lines
3.1 KiB
Markdown
166 lines
3.1 KiB
Markdown
---
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title: Filtrage et classification de nuage de points sur la base d'attributs morphologiques
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subtitle: ORASIS 2019 -- Classification
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date: May 30, 2019
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author:
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- Florent Guiotte^1,2^ 🍆 \<<florent.guiotte@uhb.fr>\>
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- Thomas Corpetti^1^
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- Sébastien Lefèvre^2^
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institute: ^1^Univ. Rennes 2 (LETG) --- ^2^IRISA (OBELIX)
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theme: metropolis
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aspectratio: 169
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bibliography: all.bib
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header-includes: |
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\usepackage{pdfpc-commands}
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\usepackage{qrcode}
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\setbeamercolor{background canvas}{bg=white}
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---
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Context
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=======
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LiDAR Point Cloud
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-----
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:::: {.columns}
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::: {.column width="50%"}
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**LiDAR Point Cloud**
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- 3D spatial data $P \in \mathbb{R}^3$
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+ Voluminous (high density point cloud)
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+ Continuously distributed
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- LiDAR features for each point
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+ Intensity
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+ Number of echoes
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+ ...
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- Multispectral
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:::
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::: {.column width="50%"}
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:::
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::::
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::: notes
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Airborne LiDAR systems are a common source of acquisition for elevation. They provide point clouds with higher and higher density.
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LiDAR point clouds consist in 3D spatial data, with a set of points defined in space by x, y and z.
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In addition to that, LiDAR point clouds comes with features extracted from the capture such as
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- the laser intensity at the returned point,
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- The number of echoes a pulse have returned, for some surfaces such as vegetation, the laser go through leaves of small branches and then can return several echoes, for example one point at the top of the canopy, and one point at the ground under the tree.
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- and other metadata from the flight
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some LiDARs are multispectral now, so we can also have several point cloud of the same scenery with intensities or number of echoes dependings of the wavelength.
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:::
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LiDAR Applications
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---------
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:::: {.columns}
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::: {.column width="50%"}
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**Urban Applications**
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- Characterize structures
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+ Buildings
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+ Vegetation
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+ ...
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- Classification
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:::
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::: {.column width="50%"}
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:::
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::::
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::: notes
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Some common LiDAR urban application are to characterize structures for
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- heat island analysis for example
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or to achieve classification
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:::
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Citations
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--------
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- Hierarchical @bosilj_partition_2018.
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- Hierarchical [@bosilj_partition_2018]
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- Hierarchical [-@bosilj_partition_2018]
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- Hierarchical @bosilj_partition_2018 [p. 3]
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Maths
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-----
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$$ \mathbb{R}^3 $$
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Other
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----
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See [context][LiDAR Point Cloud].
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```haskell
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qsort [] = []
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qsort (x:xs) = qsort (filter (< x) xs) ++ [x] ++
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qsort (filter (>= x) xs)
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```
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- [ ] Not working
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- [x] Working
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| 200 Main St.
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| Berkeley, CA 94718
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Definition
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--------
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Term 1
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: Definition 1
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Term A
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: Definition of the second term named A. Seriously.
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Term 2
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~ Definition 2a
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~ Definition 2b
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Term 2 with *inline markup*
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: Definition 2
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{ some code, part of Definition 2 }
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Third paragraph of definition 2.
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Broken
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---
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::::: {#special .sidebar}
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Here is a paragraph.
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And another.
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:::::
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::: Warning ::::::
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This is a warning.
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::: Danger
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This is a warning within a warning.
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:::
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::::::::::::::::::
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References {.allowframebreaks}
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=====
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-----
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