Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Modava, Mohammad; Akbarizadeh, Gholamreza

doi:10.1080/01431161.2016.1266104

Cited by 90 publications

(37 citation statements)

References 29 publications

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“…To minimize Equation (14), we consider a simple closed planar curve C( ) : [0, 1] → Ω parameterized by arc parameter ∈ [0, 1], then the Euler-Lagrange descent equations of E O can be obtained by embedding the curve C into a family of one parameter curves C( , t) : [0, 1] × R + → Ω and solving the following partial differential equation (PDE)…”

Section: Appendix a Derivation Process From Equation (16) To Equatiomentioning

confidence: 99%

“…However, if we use this kind of approach to process SAR image segmentation, the following two problems can be solved: (1) the strong speckle noise of SAR images, and (2) the intensity inhomogeneity of SAR images. For problem (1), some algorithms of noise reduction can be referenced or the related statistical model can be used to describe the statistical characteristics of SAR images. For problem (2), the spatial variation information of SAR images needs to be considered.…”

Section: About the Application Scope Of The Proposed Modelmentioning

confidence: 99%

“…For problem (1), some algorithms of noise reduction can be referenced or the related statistical model can be used to describe the statistical characteristics of SAR images. For problem (2), the spatial variation information of SAR images needs to be considered.…”

Section: About the Application Scope Of The Proposed Modelmentioning

confidence: 99%

“…In the past decades, SAR image segmentation technology has been widely and intensively researched as an important step in information extraction and automatic interpretation, and a variety of methods have been proposed. These proposed methods can be classified into many categories: (1) clustering-based methods such as the fuzzy c-means clustering algorithm [1], (2) threshold-based methods, including the Otsu algorithm [2], (3) specific theory-based methods, e.g., artificial neural network and deep learning, which are often applied to SAR image segmentation under complicated scenarios [3,4], (4) super pixel-based methods such as the multi-kernel joint sparse graph and multi-feature ensemble models [5,6], and (5) level set evolution (LSE)-based methods such as the active contour model (ACM) [7]. Of these existing methods, ACMs, which were first proposed by Kass et al [8], have attracted increasing attention from researchers owing to its in a level set framework.…”

Section: Introductionmentioning

confidence: 99%

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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: Appendix a Derivation Process From Equation (16) To Equatiomentioning

confidence: 99%

Section: About the Application Scope Of The Proposed Modelmentioning

confidence: 99%

Section: About the Application Scope Of The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Liu et al (2016) improved a method for coastline extraction for wide-swath SAR imagery, which used the modified K-means method and an adaptive coarse-fine object-based region-merging (MKAORM). Modava and Akbarizadeh (2017) presented a novel coastline extraction procedure from SAR images utilizing spatial fuzzy clustering and the active contour method.…”

Section: Introductionmentioning

confidence: 99%

Integrated Shoreline Extraction Approach With Use of Rasat MS and Sentinel-1a Sar Images

Demir

Erdem

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of AntalyaKemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

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Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging

Biswal¹,

Raveendra²,

Bebarta³

et al. 2022

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

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Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Cited by 90 publications

References 29 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

Integrated Shoreline Extraction Approach With Use of Rasat MS and Sentinel-1a Sar Images

Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging

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