Minimum description length synthetic aperture radar image segmentation

Galland, Frédéric; Bertaux, Nicolas; Réfrégier, Philippe

doi:10.1109/tip.2003.816005

Cited by 79 publications

(78 citation statements)

References 38 publications

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“…However, this method is quite complicated and has the side effect of boundary leakage (see Figure 3c). Fortunately, the Laplacian operator can solve this problem which has been demonstrated in [33,36]. Therefore, instead of the reinitialization process, we introduced a diffusion term "ωg∆φ" into the LSE Equation (17), and the final RD LSE equation can be expressed as…”

Section: The Proposed Modelmentioning

confidence: 99%

Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

et al. 2018

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This paper presents a method for synthetic aperture radar (SAR) image segmentation by draing upon a reaction-diffusion (RD) level set evolution (LSE) equation. The well-known RD theory consists of two main parts: reaction and diffusion terms. We first constructed the reaction term using an energy functional, which integrates the gamma statistical distribution with region-edge information from SAR images that can simultaneously suppress speckle noise and drive the active contour toward the object boundaries. Then, we used partial differential equation-based LSE to solve the proposed energy functional. Finally, a diffusion term was introduced into the LSE to ensure stability of the level set function and regularize the segmented region. The experimental results of both simulated and real SAR images showed that the proposed model has good robustness against a speckle noise as well as higher segmentation efficiency and accuracy than some existing models.

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Section: The Proposed Modelmentioning

confidence: 99%

Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

et al. 2018

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“…In the following, the purpose will thus be to generalize the MDL-based partitioning technique proposed in 18,25 to such circular data.…”

Section: Partition Into Regions With Homogeneous Orientationsmentioning

confidence: 99%

Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images

Costa

Galland²,

Roueff³

et al. 2011

SPIE Proceedings

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In this paper, which deals with textured images and more particularly with directional textures, a new parametric technique is proposed to estimate the orientation eld of textures. It consists of segmenting the image into regions with homogeneous orientations, and estimating the orientation inside each of these regions. This allows us to maximize the size of the samples used to estimate the orientation without being corrupted by the presence of boundaries between regions. For that purpose, the local -hence noisy -orientations of the texture are rst estimated using small lters (3×3 pixels). The segmentation of the obtained orientation eld image then relies on a generalization of a Minimum Description Length (MDL) based segmentation technique, to the case of π-periodic circular data modeled with Von Mises probability density functions. This leads to a fast segmentation algorithm without tuning parameters in the optimized criterion. The accuracy of the orientations estimated with the proposed method is then compared with other approaches on synthetic images and an application to the processing of real images is nally addressed.

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“…Researchers have proposed many kinds of segmentation algorithms for SAR images, which include threshold methods, 3,[17][18][19] spectral clustering (SC) algorithms, 20,21 statistic model-based methods, 1,14,15,[22][23][24][25] artificial intelligence methods, [26][27][28][29][30] support vector machine (SVM), 6,31 region growing methods, 15,[32][33][34][35] and so on. Among these algorithms, cluster-based algorithms form one popular and representative family, whose main idea is to group pixels in such a way that the pixels in the same group are more similar to each other than those in other groups.…”

Section: Introductionmentioning

confidence: 99%

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

Feng

2015

J. Appl. Remote Sens

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Abstract. Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Minimum description length synthetic aperture radar image segmentation

Cited by 79 publications

References 38 publications

Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

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