Autocorrelation features for synthetic aperture sonar image seabed segmentation

Cobb, J. Tory; Prı́ncipe, José C.

doi:10.1109/icsmc.2011.6084185

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…In the experiments from Cobb and Principe, 69 the excellent performance of wavelet coefficients in characterizing the texture information of sonar images has also been proven.…”

Section: Basic Concepts Of Waveletsmentioning

confidence: 88%

A review on the wavelet methods for sonar image segmentation

Tian

Lan

Guo

2020

International Journal of Advanced Robotic Systems

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The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.

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“…In the experiments from Cobb and Principe, 69 the excellent performance of wavelet coefficients in characterizing the texture information of sonar images has also been proven.…”

Section: Basic Concepts Of Waveletsmentioning

confidence: 88%

A review on the wavelet methods for sonar image segmentation

Tian

Lan

Guo

2020

International Journal of Advanced Robotic Systems

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“…Unser and Coulon [38] used correlation and grey level difference measure in an automatic visual inspection system of texture. Moreover, the method has been used to extract features for synthetic aperture sonar image seabed segmentation [39].…”

Section: ) Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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“…[14] presents a sand ripple model that using sinusoid functions with different orientations and scales to characterize the sand ripple pattern as three highlightshadow pairs. [21] applies autocorrelation function to extract seabed features which perform especially well for extracting periodically patterns. However, it has been reported that autocorrelation is not a suitable measure for coarseness [22] (i.e., if a texture has bigger element sizes and fewer elements are repeated, the texture is coarser [23]).…”

Section: Related Workmentioning

confidence: 99%

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

Sun,

Gerg,

Monga

2022

Preprint

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Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data, but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-theart learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method Iterative Deep Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) A deep network estimates class assignments. 2) Low-level image features from the deep network are clustered into superpixels. 3) Superpixels are clustered into class assignments (which we call pseudo-labels) using k-means. 4) Resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Because our design combines merits of classical superpixel methods with deep learning, practically we demonstrate a very significant benefit in terms of reduced selection bias, i.e. IDUS shows markedly improved robustness against the choice of training images. Finally, we also develop a semi-supervised (SS) extension of IDUS called IDSS and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery.

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Autocorrelation features for synthetic aperture sonar image seabed segmentation

Cited by 10 publications

References 16 publications

A review on the wavelet methods for sonar image segmentation

A review on the wavelet methods for sonar image segmentation

Texture Feature Extraction Methods: A Survey

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

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