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Publications about Tree Crown Extraction
Result of the query in the list of publications :
Article |
1 - A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. Pattern Recognition, 42(5): pages 699-709, May 2009.
Keywords : Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian.
@ARTICLE{Horvath09,
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| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction}, |
| year |
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{2009}, |
| month |
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{May}, |
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{Pattern Recognition}, |
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{42}, |
| number |
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{699-709}, |
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{http://dx.doi.org/10.1016/j.patcog.2008.09.008}, |
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{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Horvathetal09.pdf}, |
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{Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian} |
| } |
Abstract :
We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown
number of circles of approximately the same radius. The model has applications
to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed
using higher-order active contours (HOACs) in order to include non-trivial prior
knowledge about region shape without constraining topology. The main theoretical contribution
is an analysis of the local minima of the HOAC energy that allows us to guarantee
stable circles, fix one of the model parameters, and constrain the rest. We apply the model
to tree crown extraction from aerial images of plantations. Numerical experiments both
confirm the theoretical analysis and show the empirical importance of the prior shape information. |
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PhD Thesis and Habilitation |
1 - Etude du couvert forestier par processus ponctuels marqués.
G. Perrin. PhD Thesis, Ecole Centrale Paris, October 2006.
Keywords : Tree Crown Extraction, Marked point process, Stochastic geometry, Object extraction, RJMCMC.
@PHDTHESIS{perrin_phd06,
|
| author |
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{Perrin, G.}, |
| title |
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{Etude du couvert forestier par processus ponctuels marqués}, |
| year |
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{2006}, |
| month |
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{October}, |
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{Ecole Centrale Paris}, |
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{http://www-sop.inria.fr/ariana/personnel/Guillaume.Perrin/resume.php}, |
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{http://www-sop.inria.fr/ariana/personnel/Guillaume.Perrin/DOWNLOADS/these_perrin_2006.pdf}, |
| keyword |
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{Tree Crown Extraction, Marked point process, Stochastic geometry, Object extraction, RJMCMC} |
| } |
Résumé :
Cette thèse aborde le problème de l'extraction d'arbres à partir d'images aériennes InfraRouge Couleur (IRC) de forêts. Nos modèles reposent sur l'utilisation de processus objets ou processus ponctuels marqués. Il s'agit de variables aléatoires dont les réalisations sont des configurations d'objets géométriques. Une fois l'objet géométrique de référence choisi, nous définissons l'énergie du processus par le biais d'un terme a priori, modélisant les contraintes sur les objets et leurs interactions, ainsi qu'un terme image. Nous échantillonnons le processus objet grâce à un algorithme de type Monte Carlo par Chaînes de Markov à sauts réversibles (RJMCMC), optimisé par un recuit simulé afin d'extraire la meilleure configuration d'objets, qui nous donne l'extraction recherchée.
Dans ce manuscrit, nous proposons différents modèles d'extraction de houppiers, qui extraient des informations à l'échelle de l'arbre selon la densité du peuplement. Dans les peuplements denses, nous présentons un processus d'ellipses, et dans les zones de plus faible densité, un processus d'ellipsoïdes. Nous obtenons ainsi le nombre d'arbres, leur localisation, le diamètre de la couronne et leur hauteur pour les zones non denses. Les algorithmes automatiques résultant de cette modélisation sont testés sur des images IRC très haute résolution fournies par l'Inventaire Forestier National (IFN). |
Abstract :
This thesis addresses the problem of tree crown extraction from Colour InfraRed (CIR) aerial images of forests. Our models are based on object processes, otherwise known as marked point processes. These mathematical objects are random variables whose realizations are configurations of geometrical shapes. This approach yields an energy minimization problem, where the energy is composed of a regularization term (prior density), which introduces some constraints on the objects and their interactions, and a data term, which links the objects to the features to be extracted. Once the reference object has been chosen, we sample the process and extract the best configuration of objects with respect to the energy, using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm embedded in a Simulated Annealing scheme.
We propose different models for tree crown extraction depending on the density of the stand. In dense areas, we use an ellipse process, while in sparse vegetation an ellipsoïd process is used. As a result we obtain the number of stems, their position, the diameters of the crowns and the heights of the trees for sparse areas. The resulting algorithms are tested on high resolution CIR aerial images provided by the French National Forest Inventory (IFN). |
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12 Conference articles |
1 - A `Gas of Circles' Phase Field Model and its Application to Tree Crown Extraction.
P. Horvath and I. H. Jermyn. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007.
Keywords : Phase Field, Tree Crown Extraction.
@INPROCEEDINGS{Horvath07d,
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| author |
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{Horvath, P. and Jermyn, I. H.}, |
| title |
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{A `Gas of Circles' Phase Field Model and its Application to Tree Crown Extraction}, |
| year |
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{2007}, |
| month |
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{September}, |
| booktitle |
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{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
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{Poznan, Poland}, |
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{Phase Field, Tree Crown Extraction} |
| } |
Abstract :
The problem of extracting the region in the image domain
corresponding to an a priori unknown number of circular objects
occurs in several domains. We propose a new model of a `gas of
circles', the ensemble of regions in the image domain composed of
circles of a given radius. The model uses the phase field
reformulation of higher-order active contours (HOACs). Phase fields
possess several advantages over contour and level set approaches to
region modelling, in particular for HOAC models. The reformulation
allows us to benefit from these advantages without losing the
strengths of the HOAC framework. Combined with a suitable likelihood
energy, and applied to the tree crown extraction problem, the new
model shows markedly improved performance, both in quality of
results and in computation time, which is two orders of magnitude
less than the HOAC level set implementation.
|
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2 - A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction.
P. Horvath. In Proc. Advanced Concepts for Intelligent Vision Systems, Delft, Netherlands, August 2007.
Keywords : Higher-order, Tree Crown Extraction, Colour.
@INPROCEEDINGS{Horvath07c,
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{A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction}, |
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{2007}, |
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{Proc. Advanced Concepts for Intelligent Vision Systems}, |
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{Delft, Netherlands}, |
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| } |
Abstract :
Forestry management makes great use of statistics concerning the
individual trees making up a forest, but the acquisition of this
information is expensive. Image processing can potentially both
reduce this cost and improve the statistics. The key problem is the
delineation of tree crowns in aerial images. The automatic solution
of this problem requires considerable prior information to be built
into the image and region models. Our previous work has focused on
including shape information in the region model; in this paper we
examine the image model. The aerial images involved have three
bands. We study the statistics of these bands, and construct both
multispectral and single band image models. We combine these with a
higher-order active contour model of a `gas of circles' in order to
include prior shape information about the region occupied by the
tree crowns in the image domain. We compare the results produced by
these models on real aerial images and conclude that multiple bands
improves the quality of the segmentation. The model has many other
potential applications, e.g. to nano-technology, microbiology,
physics, and medical imaging.
|
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3 - A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images.
P. Horvath and I. H. Jermyn. In Proc. International Conference on Computer Analysis of Images and Patterns (CAIP), Vienna, Austria, August 2007.
Keywords : Phase Field, Tree Crown Extraction.
@INPROCEEDINGS{Horvath07b,
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| title |
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{A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images}, |
| year |
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{Phase Field, Tree Crown Extraction} |
| } |
Abstract :
We describe a model for tree crown extraction from aerial images, a
problem of great practical importance for the forestry industry. The
novelty lies in the prior model of the region occupied by tree
crowns in the image, which is a phase field version of the
higher-order active contour inflection point `gas of circles' model.
The model combines the strengths of the inflection point model with
those of the phase field framework: it removes the `phantom circles'
produced by the original `gas of circles' model, while executing two
orders of magnitude faster than the contour-based inflection point
model. The model has many other areas of application e.g., to
imagery in nanotechnology, biology, and physics. |
|
4 - Circular object segmentation using higher-order active contours.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07), Debrecen, Hungary, January 2007.
Note : In Hungarian
Keywords : Higher-order, Tree Crown Extraction, Shape.
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| keyword |
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{Higher-order, Tree Crown Extraction, Shape} |
| } |
|
5 - An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP), Madurai, India, December 2006.
Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape.
@INPROCEEDINGS{Horvath06_icvgip,
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{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
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{An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction}, |
| year |
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{2006}, |
| month |
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{Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP)}, |
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{Madurai, India}, |
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{http://dx.doi.org/10.1007/11949619_14}, |
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{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath06_icvgip.pdf}, |
| keyword |
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{Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} |
| } |
Abstract :
A central task in image processing is to find the
region in the image corresponding to an entity. In a
number of problems, the region takes the form of a
collection of circles, eg tree crowns in remote
sensing imagery; cells in biological and medical
imagery. In~citeHorvath06b, a model of such regions,
the `gas of circles' model, was developed based on
higher-order active contours, a recently developed
framework for the inclusion of prior knowledge in
active contour energies. However, the model suffers
from a defect. In~citeHorvath06b, the model
parameters were adjusted so that the circles were local
energy minima. Gradient descent can become stuck in
these minima, producing phantom circles even with no
supporting data. We solve this problem by calculating,
via a Taylor expansion of the energy, parameter values
that make circles into energy inflection points rather
than minima. As a bonus, the constraint halves the
number of model parameters, and severely constrains one
of the two that remain, a major advantage for an
energy-based model. We use the model for tree crown
extraction from aerial images. Experiments show that
despite the lack of parametric freedom, the new model
performs better than the old, and much better than a
classical active contour. |
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6 - 2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong-Kong, August 2006.
Keywords : Data energy, Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process.
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{2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes}, |
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{Proc. International Conference on Pattern Recognition (ICPR)}, |
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| } |
Abstract :
High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the forest resources, from which can be deduced information of biodiversity and ecological sustainability. In that prospect, automatic algorithms are needed to give a further exploitation of the data and to assist human operators. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the positions of the trees and marks their geometric features. This approach gives also the number of stems, their position, and their size. It is an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Results are shown on aerial images provided by the French National Forest Inventory (IFN). |
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7 - A Higher-Order Active Contour Model for Tree Detection.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong Kong, August 2006.
Keywords : Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction.
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{A Higher-Order Active Contour Model for Tree Detection}, |
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| } |
Abstract :
We present a model of a ‘gas of circles’, the ensemble
of regions in the image domain consisting of an
unknown number of circles with approximately fixed
radius and short range repulsive interactions, and
apply it to the extraction of tree crowns from aerial
images. The method uses the re- cently introduced
‘higher order active contours’ (HOACs), which
incorporate long-range interactions between contour
points, and thereby include prior geometric
information without using a template shape. This makes
them ideal when looking for multiple instances of an
entity in an image. We study an existing HOAC model
for networks, and show via a stability calculation
that circles stable to perturbations are possible
for constrained parameter sets. Combining this prior
energy with a data term, we show results on aerial
imagery that demonstrate the effectiveness of the
method and the need for prior geometric knowledge. The
model has many other potential applications. |
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8 - A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests.
M. Eriksson and G. Perrin and X. Descombes and J. Zerubia. In Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Marne La Vallee, France, July 2006.
Keywords : Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction.
@INPROCEEDINGS{eriksson06a,
|
| author |
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{Eriksson, M. and Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
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{A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_eriksson06a.pdf}, |
| keyword |
= |
{Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction} |
| } |
Abstract :
Most of today's silviculture methods has the goal to optimise the outcome of the forest in stem volume when it is cut. It might also be relevant to save parts of the forest, for instance, to protect a habitat. In order to get a good survey of the forest, remote sensed images are often used. These images are most often manually interpreted in combination with field measurements in order to estimate the forest parameters that are of importance in the decision how to optimally maintain the forest. Among these parameters the most common are stem number, stem volume, and tree species. Interpretation of images are often labour and time consuming. Thus, automatically developed methods for interpretation can lower the work load and speed up the interpretation time.
The interpretation is often done using images captured from a far distance from the ground in order to capture as large area as possible. However, this lower the accuracy of the estimates since it must be done stand wise. Knowledge of where each individual trees in the forest is located together with its size will increase accuracy. It makes it also possible to plan the cutting in detail. With this knowledge in mind, research about finding automatically methods for finding individual tree crowns in aerial images has been a subject for researchers the last decades.
Today's methods are not capable to alone handle all kind of forests. Therefore, comparative studies of different segmentation methods with different types of forests are of importance in order to clarify how much a method is reliable at a certain type of forest. This knowledge can, for instance, be used to build up an expert system which are supposed to be able to find individual tree crowns in any kind of forests. The comparison is done using images covering different types of forests. The types of forests that are included in the study ranges from isolated tree crown where the ground is clearly visible between the crowns to dense forest which is naturally regenerated via planted forest.
In this study we compare three existing segmentation methods for extracting individual tree crowns from aerial images. The first two methods are probabilistic methods which minimises some energy function while the third is a region growing algorithm. The first probabilistic method is based on a Markov Random Field modelling. We define a prior Markov model to segment the image into three classes (background, vegetation and tree centres). The prior model embed a circular shape model of the tree crown with a random radius. The data term allows to well position the tree centres onto the image and to describe the tree shape as fluctuations around the circular template. Besides, some long range interactions models the relations between the trees locations, such as some periodicity in case of plantations.
The second probabilistic method consists in modeling the trees in the forestry images as random configurations of ellipses or ellipsoids, whose points are the positions of the stems and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. We estimate the best configuration of an unknown number of objects, from which 2D and 3D vegetation resource parameters can be extracted. To sample this marked point process, we use Monte Carlo dynamics, while the optimization is performed via a Simulated Annealing algorithm, which results in a fully automatic approach. This approach works well on plantations, where there are high spatial relations between the trees, and on isolated trees where 3D parameters can be extracted, but some difficulties remain in dense areas.
The third method, the region growing algorithm, relies as all region growing methods on good seed points, i.e. in this case approximate locations of the tree crowns. From the seed points the segments are grown according to a grey level value of the neighbouring pixels. The larger the value is the sooner it is connected to the neighbouring segment. The segments stops to grow when all pixels belongs to a segment. This method, contrary the others, will have as a result, segments that have captured the actual shape of the tree crown if the forest is not too sparse. If the forest is too sparse such that the ground is visible, there are problems of finding the seed points. In the cases when the forest is sparse, there are difficulties to separate the tree crowns from the ground. Even if the seed points would be located only at the tree crowns the result will contain a lot of errors since all pixels most belong to a segment, i.e. even the ground pixels must be connected to a segment in this case. |
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