Mean–Standard Deviation Representation of Sonar Images for Echo Detection: Application to SAS Images

Maussang, Frédéric; Chanussot, Jocelyn; Hétet, A.; Amate, Maud

doi:10.1109/joe.2007.907936

Cited by 37 publications

(30 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly used model in son distribution [3], [7]- [9]. A Weibull PDF ha and can achieve a better fitting to the distribution and exponential distribution are of Weibull distribution.…”

Section: B Non-rayleigh Modelsmentioning

confidence: 99%

“…Accordingly, statistical description of speckle is an important part in SAS image processing. The most widely used speckle model is Weibull distribution [3], [7]- [9], which is simple, fast, and suitable for traditional segmentation framework, but limited performance. A Rayleigh mixture model (RMM) consists of several Rayleigh component and offers better description of acoustic echoes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Underwater object highlight segmentation in SAS image using Rayleigh mixture model

Zhai

Jiang

Zhang

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

View full text Add to dashboard Cite

Image segmentation, which usually employs a statistical model, is an essential step in synthetic aperture sonar (SAS) image processing. This work addresses the Rayleigh mixture model (RMM), representing SAS underwater amplitude image. High resolution SAS image of detected artificial object is segmented using RMM and Markov random field (MRF) model. We present a quick unsupervised iterative method to segment the object (highlight). In each iteration, RMM parameter is estimated by EM algorithm, and used by graph-cut based MRF image segmentation. The algorithm converges, and gives the final segmentation. Experiment on real SAS data shows that the RMM is capable of describing complex object echo distribution, thus improve the segmentation quality of SAS image.Index Terms-Rayleigh mixture model (RMM), Markov random field (MRF), Image segmentation.

show abstract

Section: B Non-rayleigh Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater object highlight segmentation in SAS image using Rayleigh mixture model

Zhai

Jiang

Zhang

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

View full text Add to dashboard Cite

show abstract

“…It provides a segmentation method based on the statistical properties mentioned previously to isolate the echoes from the reverberation background on the SAS images [4]. The method is decomposed into the following steps.…”

Section: Segmentationmentioning

confidence: 99%

“…And then the mean-standard deviation segmentation method [4] is applied to get the binary images by considering the properties. Mathematical morphological operations [5] are applied on the binary images and areas of connected components in the images are extracted as features.…”

Section: Introductionmentioning

confidence: 99%

An automatic targets detection method from SAS images based on mean-standard deviation

Hui

Zhang

et al. 2010

2010 3rd International Congress on Image and Signal Processing

View full text Add to dashboard Cite

Targets detection is important for the analysis of Synthetic Aperture Sonar (SAS) images. An automatic targets detection method from SAS images is presented by analyzing the statistical properties of the images. The method mainly has three procedures: preprocessing of SAS images, especially the mean-standard deviation segmentation method to binarize the images, extracting areas of connected components in the binary images as features, maximum area of connected components method detecting. At last, the targets detection method based on mean-standard deviation is applied to lake-trial datasets for validation.

show abstract

“…This method is based on features extracted by using Gaussian Markov random fields and effectively removes speckle noise from the sonar image. Maussang et al [15], [16] have applied adaptive data thresholding for object detection as well as statistical methods that do not require the presence of a shadow for mine detection, which is useful for detection of buried mines. Dee et al [17] have presented a survey of the recent advances in the application of graphical models (e.g., Markov Bayesian networks) for the purpose of real-time visual surveillance.…”

mentioning

confidence: 99%

Symbolic Analysis of Sonar Data for Underwater Target Detection

Mukherjee

Gupta

Ray

et al. 2011

IEEE J. Oceanic Eng.

View full text Add to dashboard Cite

Abstract-This paper presents a symbolic pattern analysis method for robust feature extraction from sidescan sonar images that are generated from autonomous underwater vehicles (AUVs). The proposed data-driven algorithm, built upon the concepts of symbolic dynamics and automata theory, is used for detection of mines and mine-like objects in the undersea environment. This real-time algorithm is based on symbolization of the data space via coarse graining, i.e., partitioning of the two-dimensional sonar images. The statistical information, in terms of stochastic matrices that serve as features, is extracted from the symbolized images by construction of probabilistic finite state automata. A binary classifier is designed for discrimination of detected objects into mine-like and nonmine-like categories. The pattern analysis algorithm has been validated on sonar images generated in the exploration phase of a mine hunting operation; these data have been provided by the Naval Surface Warfare Center. The algorithm is formulated for real-time execution on limited-memory commercial-of-the-shelf platforms and is capable of detecting objects on the seabed-bottom.

show abstract

Mean–Standard Deviation Representation of Sonar Images for Echo Detection: Application to SAS Images

Cited by 37 publications

References 29 publications

Underwater object highlight segmentation in SAS image using Rayleigh mixture model

Underwater object highlight segmentation in SAS image using Rayleigh mixture model

An automatic targets detection method from SAS images based on mean-standard deviation

Symbolic Analysis of Sonar Data for Underwater Target Detection

Contact Info

Product

Resources

About