Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A graph cut approach to 3D tree delineation, using integrated airborne
  LiDAR and hyperspectral imagery   Recognising individual trees within remotely sensed imagery has important
applications in forest ecology and management. Several algorithms for tree
delineation have been suggested, mostly based on locating local maxima or
inverted basins in raster canopy height models (CHMs) derived from Light
Detection And Ranging (LiDAR) data or photographs. However, these algorithms
often lead to inaccurate estimates of forest stand characteristics due to the
limited information content of raster CHMs. Here we develop a 3D tree
delineation method which uses graph cut to delineate trees from the full 3D
LiDAR point cloud, and also makes use of any optical imagery available
(hyperspectral imagery in our case). First, conventional methods are used to
locate local maxima in the CHM and generate an initial map of trees. Second, a
graph is built from the LiDAR point cloud, fused with the hyperspectral data.
For computational efficiency, the feature space of hyperspectral imagery is
reduced using robust PCA. Third, a multi-class normalised cut is applied to the
graph, using the initial map of trees to constrain the number of clusters and
their locations. Finally, recursive normalised cut is used to subdivide, if
necessary, each of the clusters identified by the initial analysis. We call
this approach Multiclass Cut followed by Recursive Cut (MCRC). The
effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a
coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK.
The performance of MCRC was usually superior to that of other delineation
methods, and was further improved by including high-resolution optical imagery.
Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual
crown characteristics to be measured. By making full use of the data available,
graph cut has the potential to considerably improve the accuracy of tree
delineation.

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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: eess