@Conference{Bea2014a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Tensor Based Mean-Shift Polsar Image Enhancement},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
volume = {IGARSS 2014},
publisher = {},
url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=\&arnumber=6947503},
isbn = {},
doi = {10.1109/IGARSS.2014.6947503},
address = {Quebec City, QC},
pages = {4544-4547},
year = {2014},
month = {July 13-18,},
abstract = {The mean-shift approach uses a local estimation of the pdf and moves every data points toward the modes. The direction is calculated from the mean value of surrounding points weighted by a Gaussian kernel. An advantage of the technique is that both radiometric and spatial information could be used in the weighted mean calculation. For polarimetric SAR images, we use likelihood ratio as radiometric similarity or distance measure. The spatial distance between pixels is also used with a Gaussian weight. Contours are well preserved because pixels on one side are dissimilar to pixels on the other side. To improve contour preservation, we examine how the tensor of pixel position can be integrated into the weight calculation. The tensor is calculated from weighted pixel position inside a window. Good PolSAR image smoothing is obtained.},
mypdf = {11},
slide = {https://BeaulieuJM.ca/slide/slideBea2014a.pdf},
keywords = {}
}

@Conference{Bea2010a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Mean-shift and Hierarchical Clustering for Textured Polarimetric {SAR} Image Segmentation/Classification},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
volume = {IGARSS 2010},
publisher = {},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5653919},
isbn = {978-1-4244-9565-8},
doi = {10.1109/IGARSS.2010.5653919},
address = {Honolulu, HI},
pages = {2519-2522},
year = {2010},
month = {July 25-30,},
abstract = {Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.},
mypdf = {11},
slide = {https://BeaulieuJM.ca/slide/slideBea2010a.pdf},
keywords = {9-look polarimetric SAR image; hierarchical clustering; hierarchical grouping; image analysis; image classification; image segmentation; image texture; K distribution; mean-shift step; nontextured image region; pattern clustering; radar imaging; radar polarimetry; segment mean value; statistical distributions; synthetic aperture radar; textured polarimetric SAR image; unsupervised classification; Wishart distribution}
}

@Conference{Bom2009a,
author = {Bombrun, Lionel and Beaulieu, Jean-Marie and Vasile, G and Ovarlez, J P and Pascal, F and Gay, M},
editor = {},
title = {Hierarchical Segmentation of Polarimetric {SAR} Images using Heterogeneous Clutter Models},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009},
volume = {III},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/5418271},
isbn = {978-1-4244-3394-0},
doi = {10.1109/IGARSS.2009.5418271},
address = {Cape Town, South Africa},
pages = {5-8},
year = {2009},
month = {12-17 July},
abstract = {In this paper, heterogeneous clutter models are introduced to describe Polarimetric Synthetic Aperture Radar (PolSAR) data. Based on the Spherically Invariant Random Vectors (SIRV) estimation scheme, the scalar texture parameter and the normalized covariance matrix are extracted. If the texture parameter is modeled by a Fisher PDF, the observed target scattering vector follows a KummerU PDF. Then, this PDF is implemented in a hierarchical segmentation algorithm. Segmentation results are shown on high resolution PolSAR data at L and X band.},
mypdf = {13},
keywords = {Backscatter; Clutter; Covariance matrix; Data mining; Fisher PDF; geophysical image processing; geophysical techniques; heterogeneous clutter models; hierarchical image segmentation; hierarchical segmentation algorithm; image segmentation; image texture; KummerU PDF; L band high resolution PolSAR data; L-band; Maximum likelihood estimation; normalized covariance matrix; Parameter estimation; polarimetric SAR images; polarimetric synthetic aperture radar data; PolSAR data; radar clutter; radar polarimetry; Radar scattering; remote sensing by radar; scalar texture parameter; Segmentation; Spherically Invariant Random Vectors; spherically invariant random vectors estimation scheme; synthetic aperture radar; target scattering vector; Testing; X band high resolution PolSAR data},
openpdf = {https://hal.archives-ouvertes.fr/hal-00398923/},
openid = {HAL archives-ouvertes}
}

@Conference{Bea2008a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Classification of Polarimetric {SAR} Images using Radiometric and Texture Information},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium IGARSS 2008},
volume = {IV},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/document/4779648},
isbn = {978-1-4244-2807-6},
doi = {10.1109/IGARSS.2008.4779648},
address = {Boston, MA, USA},
pages = {29-32},
year = {2008},
month = {July 7-11},
abstract = {},
mypdf = {11},
slide = {https://BeaulieuJM.ca/slide/slideBea2008a.pdf},
keywords = {classification; classification map; clustering; Clustering algorithms; clustering process; Covariance matrix; geophysical techniques; hierarchical clustering; hierarchical segmentation; image classification; image segmentation; Iterative algorithms; K distribution; mean shift clustering; mean-shift; Merging; Partitioning algorithms; pattern clustering; Pixel; polarimetric SAR image; Probability; radar polarimetry; radiometry; Remote sensing; remote sensing by radar; scalar texture component; synthetic aperture radar; texture; texture information; Wishart distribution}
}

@Conference{Bom2008b,
author = {Bombrun, Lionel and Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation of Polarimetric {SAR} Data based on the Fisher Distribution for Texture Modeling},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008},
volume = {V},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/4780100},
isbn = {978-1-4244-2807-6},
doi = {10.1109/IGARSS.2008.4780100},
address = {Boston, MA, USA},
pages = {350-353},
year = {2008},
month = {July 7-11},
abstract = {The Polarimetric Synthetic Aperture Radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured scenes, the product model is used and the texture component is often modeled by a Gamma distribution. In this paper, authors propose to use the Fisher distribution for texture modeling. From a Fisher distributed texture component, we derive the distribution of the complex covariance matrix and we propose to implement the KummerU distribution in a hierarchical segmentation and a hierarchical clustering algorithm. Segmentation and classification results are shown on synthetic images and on ESAR L-band PolSAR data over the Oberpfaffenhofen test-site.},
mypdf = {13},
keywords = {Classification; Clustering algorithms; complex Wishart distribution; covariance matrices; covariance matrix; Electromagnetic scattering; ESAR L-band PolSAR data; Fisher distribution; Gamma distribution; geophysical techniques; geophysics computing; hierarchical clustering algorithm; hierarchical segmentation; image classification; image segmentation; image texture; KummerU; KummerU distribution; L-band; Layout; Oberpfaffenhofen test-site; Polarimetric SAR images; Polarimetric Synthetic Aperture Radar data; Polarization; radar polarimetry; Radar scattering; Receiving antennas; remote sensing by radar; Segmentation; Speckle; synthetic aperture radar; Texture; texture component; texture modeling},
openpdf = {https://hal.archives-ouvertes.fr/hal-00369374/},
openid = {HAL archives-ouvertes}
}

@Conference{Bea2003a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Segmentation of Textured Scenes using Polarimetric {SAR}s},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, GARSS'03},
volume = {1},
publisher = {},
url = {https://ieeexplore.ieee.org/document/1293804},
isbn = {},
doi = {10.1109/IGARSS.2003.1293804},
address = {Toulouse, France},
pages = {446-448},
year = {2003},
month = {21-25 July},
abstract = {},
mypdf = {11},
keywords = {}
}

@Conference{Bea2002,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Hierarchical Segmentation of Polarimetric {SAR} Images},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS'02},
volume = {5},
publisher = {},
url = {https://ieeexplore.ieee.org/document/1026710},
isbn = {},
doi = {10.1109/IGARSS.2002.1026710},
address = {Toronto, ON, Canada},
pages = {2590-2592},
year = {2002},
month = {24-28 June},
abstract = {A hierarchical stepwise optimization process is used for polarimetric SAR image segmentation. The process starts with small sets of pixels as segments and then sequentially merges the segment pair that minimises a stepwise criterion. The polarimetric information could be represented by a covariance matrix. The proposed criterion is based upon the testing of the equality of covariance matrices of adjacent regions. The segmentation of SAR images is greatly complicated by the presence of coherent speckle. We are using spatial constraints and contour shapes in order to improve the segmentation results.},
mypdf = {11},
slide = {https://BeaulieuJM.ca/slide/slideBea2002.pdf},
keywords = {}
}

@Conference{Bea1990a,
author = {Beaulieu, Jean-Marie},
editor = {},
title = {Versatile And Efficient Hierarchical Clustering For Picture Segmentation},
booktitle = {International Geoscience and Remote Sensing Symposium, IGARSS'90},
volume = {},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/document/688832},
isbn = {},
doi = {10.1109/IGARSS.1990.688832},
address = {The University of Maryland, College Park,Maryland},
pages = {1663-1663},
year = {1990},
month = {May 20-24},
abstract = {},
mypdf = {11},
keywords = {Approximation algorithms; Approximation error; Bismuth; Clustering algorithms; Corporate acquisitions; Data structures; Image segmentation; Partitioning algorithms; Probability; Shape}
}

@Conference{Bel1989,
author = {Belaid, M Ait and Thomson, K P B and Edwards, Geoffrey and Beaulieu, Jean-Marie},
editor = {},
title = {Sementation d'image spot integree a l'information cartographique en vu de l'etablissment de la carte d'utilization de sol au maroc},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS'89},
volume = {1},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/document/567151},
isbn = {},
doi = {10.1109/IGARSS.1989.567151},
address = {Vancouver, Canada},
pages = {56-59},
year = {1989},
month = {July 10-14},
abstract = {This paper is concerned with the integration of remote sensing and conventional data. It presents a purely digital method of merging a multispectral SPOT image and field boundaries. This yields a product which is a new image registered to the national grid of Morocco, having four channels with images resampled to 10 m. The fourth channel contains the field boundaries which are digitized using the spatial information system PAMAP.
A Hierarchical Step-Wise Optimization (HSWO) algorithm developed by Beaulieu is applied to the new four band ``image'' to test the capability of the segmentation to map the land use and to provide the crop inventory in small areas of land.},
mypdf = {11},
keywords = {}
}

@Conference{Edw1989,
author = {Edwards, Geoffrey and Beaulieu, Jean-Marie},
editor = {},
title = {Segmentation of {SAR} Imagery Containing Forest Clear Cuts},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS'89},
volume = {3},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/document/576042},
isbn = {},
doi = {10.1109/IGARSS.1989.576042},
address = {Vancouver, Canada},
pages = {1195-1197},
year = {1989},
month = {July 10-14},
abstract = {A Hierarchical Step-Wise Optimisation (HSWO) algorithm has been adapted to the problem of identifying and mapping forest clear cuts in synthetic aperture radar (SAR) C-band imagery.
Preliminary results are presented. The mean grey level of a segment is the most useful segment discriminator, especially for recent clear cuts, but relative segment size and the ratio of perimeter length to surface area (P/A) appear to be useful secondary discriminators. A filtered image which is segmented appears to be the most reliable for locating clear cuts, whereas the unfiltered image, when segmented, yields better boundary information. A method for combining both segment partitions is presented. All clear cuts in the sample were identified. Surface areas concord with manually estimated values.},
mypdf = {13},
keywords = {Clouds; Forestry; Image segmentation; Information filtering; Information filters; Layout; Partitioning algorithms; Pixel; Satellites; Synthetic aperture radar}
}