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Publications of 2006
Result of the query in the list of publications :
15 Conference articles |
2 - Burnt area mapping using Support Vector Machines.
O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Forest Fire Research, Figueira da Foz, Portugal, November 2006.
Keywords : Satellite images, Forest fires, Burnt areas, Support Vector Machines.
@INPROCEEDINGS{zammit_icffr_06,
|
| author |
= |
{Zammit, O. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Burnt area mapping using Support Vector Machines}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. International Conference on Forest Fire Research}, |
| address |
= |
{Figueira da Foz, Portugal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_zammit_icffr_06.pdf}, |
| keyword |
= |
{Satellite images, Forest fires, Burnt areas, Support Vector Machines} |
| } |
|
3 - An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), Atlanta, October 2006.
Keywords : 3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images.
Copyright : IEEE
@INPROCEEDINGS{lafarge_icip06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Atlanta}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_icip06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images} |
| } |
|
4 - Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval.
A. Bhattacharya and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. compImage, Coimbra, Portugal, October 2006.
Keywords : Road network, Indexation, Semantic, Retrieval, Feature statistics.
@INPROCEEDINGS{bhatta_compimage06,
|
| author |
= |
{Bhattacharya, A. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. compImage}, |
| address |
= |
{Coimbra, Portugal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_bhatta_compimage06.pdf}, |
| keyword |
= |
{Road network, Indexation, Semantic, Retrieval, Feature statistics} |
| } |
Abstract :
Retrieval from remote sensing image archives relies on the
extraction of pertinent information from the data about the entity of interest (e.g. land cover type), and on the robustness of this extraction to nuisance variables (e.g. illumination). Most image-based characterizations are not invariant to such variables. However, other semantic entities in the image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. Road networks are one example: their properties vary considerably, for example, from urban to rural areas. This paper takes the first steps towards classification (and hence retrieval) based on this idea. We study the dependence of a number of network features on the class of the image ('urban' or 'rural'). The chosen features include measures of the network density, connectedness, and `curviness'. The feature distributions of the two classes are well separated in feature space, thus providing a basis for retrieval. Classification using kernel k-means confirms this conclusion. |
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5 - Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture.
J. Aubray and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, September 2006.
Keywords : Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics.
@INPROCEEDINGS{aubray_eusipco06,
|
| author |
= |
{Aubray, J. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Florence, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_aubray_eusipco06.pdf}, |
| keyword |
= |
{Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics} |
| } |
Abstract :
Probabilistic adaptive wavelet packet models of
texture pro- vide new insight into texture structure
and statistics by focus- ing the analysis on
significant structure in frequency space. In very
adapted subbands, they have revealed new bimodal
statistics, corresponding to the structure inherent to
a texture, and strong dependencies between such
bimodal sub- bands, related to phase coherence in a
texture. Existing models can capture the former but
not the latter. As a first step to- wards modelling
the joint statistics, and in order to simplify earlier
approaches, we introduce a new parametric family of
models capable of modelling both bimodal and unimodal
subbands, and of being generalized to capture the
joint statistics. We show how to compute MAP estimates
for the adaptive basis and model parameters, and apply
the models to Brodatz textures to illustrate their
performance. |
|
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.
@INPROCEEDINGS{perrin_06_c,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes}, |
| year |
= |
{2006}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Hong-Kong}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_perrin_06_c.pdf}, |
| keyword |
= |
{Data energy, Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process} |
| } |
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). |
|
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.
@INPROCEEDINGS{horvath_icpr06,
|
| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{A Higher-Order Active Contour Model for Tree Detection}, |
| year |
= |
{2006}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Hong Kong}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_horvath_icpr06.pdf}, |
| keyword |
= |
{Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction} |
| } |
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. |
|
8 - Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS), Marne La Vallee, France, July 2006.
Keywords : 3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations.
@INPROCEEDINGS{lafarge_isprs06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_isprs06.pdf}, |
| keyword |
= |
{3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations} |
| } |
|
9 - 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 |
= |
{Eriksson, M. and Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{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. |
|
10 - An Automatic 3D City Model : a Bayesian Approach using Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 2006.
Note : Copyright IEEE
Keywords : 3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM).
@INPROCEEDINGS{florenticassp06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic 3D City Model : a Bayesian Approach using Satellite Images}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Toulouse, France}, |
| note |
= |
{Copyright IEEE}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_florenticassp06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM)} |
| } |
|
11 - Forest Resource Assessment using Stochastic Geometry.
G. Perrin and X. Descombes and J. Zerubia and J.G. Boureau. In Proc. International Precision Forestry Symposium, March 2006.
Keywords : Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy.
@INPROCEEDINGS{perrin_06_b,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J. and Boureau, J.G.}, |
| title |
= |
{Forest Resource Assessment using Stochastic Geometry}, |
| year |
= |
{2006}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. International Precision Forestry Symposium}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_ipfs06.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy} |
| } |
Abstract :
Aerial and satellite imagery has a key role to play in natural resource management, especially in forestry application. The submetric resolution of the data enables to study forests at the scale of trees, and to get a more accurate assessment of the resources such as the number of stems or the forest cover. To develop automatic tools in order to help the inventories in their work and to bring more knowledge about the stands is also nowadays of important economical and environmental concerns.
In this paper, we aim at extracting tree crowns from high resolution aerial Color Infrared images (CIR) of forests using marked point processes. Our approach consists in modelling the trees in the forestry images as random configurations of ellipses, 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. Our goal is to find the best configuration of an unknown number of objects, i.e. the configuration that maximizes this density. 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.
We present different models for the data term in order to cope with different kinds of stands : plantations, isolated trees and mixed stands. Results are shown on aerial CIR images provided by the French Forest Inventory (IFN) |
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