&lt;title&gt;Sea mine detection and classification using side-looking sonar&lt;/title&gt;

Hyland, John C.; Dobeck, Gerald J.

doi:10.1117/12.211341

Cited by 38 publications

(17 citation statements)

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“…For an early survey of active target detection, we refer the reader to [5] consisting of statistical and signal processing approaches that assume availability of the full target field/signature (see also [6,7]). The field of anomaly detection [8] further generalizes the scope of target detection and employs tools from machine learning, e.g.…”

Section: Related Workmentioning

confidence: 99%

Analysis of target detection via matrix completion

Choudhary

Mitra

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A problem of broad interest is the detection and localization of a target or object from its generated field. In this paper, a detection and localization strategy which exploits the structure of target fields is designed and analyzed. In taking advantage of this structure, one is able to reduce sample complexity requirements while maintaining good performance. In particular, an exploration-exploitation approach to target detection is proposed utilizing the theory of low-rank matrix completion for a decaying separable target field. The assumptions on the field are fairly generic and are applicable to many decay profiles. Our approach does not require specific knowledge of the field, only that it admits a rank-one representation. A performance analysis for localization is presented that characterizes a trade-off with sample complexity in the presence of noise.

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Section: Related Workmentioning

confidence: 99%

Analysis of target detection via matrix completion

Choudhary

Mitra

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The idea is that the coherence pattern extracted from the UXO objects differ from those of the non-UXO objects, hence aiding the overall classification process. The theme of the other framework is multi-aspect classification using either (a) a decision-level multi-aspect fusion [44], which linearly or non-linearly combines the individual classification decisions, generated at several aspects, or (b) a feature-level multi-aspect fusion [12] using hidden Markov model (HMM) to generate one decision based upon observing a sequence of AC feature vectors at various aspects with certain separations, or (c) a collaborative decisionmaking process [43], which uses a combination of the feature-level and decision-level fusion methods. It would be prudent to study and test these different methods and compare their results with the multi-aspect MSC classifier in (4.4).…”

Section: Future Workmentioning

confidence: 99%

Underwater UXO classification using Matched Subspace Classifier with synthetic sparse dictionaries

Hall

Azimi-Sadjadi

Kargl

2016

OCEANS 2016 MTS/IEEE Monterey

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UNDERWATER UXO CLASSIFICATION USING MATCHED SUBSPACE CLASSIFIER WITH SYNTHETIC SPARSE DICTIONARIESClassification of underwater objects such as unexploded ordnances (UXO) and mines from sonar datasets poses a difficult problem. Among factors that complicate classification of these objects are: variations in the operating and environmental conditions, presence of spatially varying clutter, variations in target shape, composition, orientation and burial conditions. Furthermore, collection of large quantities of real and representative data for training and testing in various background conditions is very difficult and impractical in many cases. In order to remedy the lack of data availability, physical models of varying computational complexity are often used to supplement training databases with synthetically created samples which predict the response of known target models.In this thesis, we try to address several key questions for designing robust classifiers for UXO and munitions classification from low frequency sonar. These include: (1) "How can we form discriminative and highly separable features for describing UXO and non-UXO objects in a given dataset?", (2) "When do we reach a point of diminishing returns when utilizing synthetic models in a classifier's training?", and more importantly (3) "Which types of object variations cannot be modeled well by synthetic data?". Although, it may be somewhat ambitious to expect model data to capture all the essential features of proud or buried underwater objects for target characterization, these models can nevertheless provide us with clues on how to augment the training datasets to improve the robustness in different environmental conditions.ii Using empirically validated scattering models developed by University of Washington's Applied Physics Laboratory (APL-UW), fast ray models were acquired to generate the required synthetic training dataset for various UXO and non-UXO objects. A comprehensive analysis is then carried out on the classification performance of two subspace matching classifiers, trained on the synthetic data generated from this physical model, and tested on three real underwater sonar datasets. Both single and multi-aspect classification were considered using a combination of linear subspace models. Our classification hypothesis is that the spectral content of sonar backscatter display unique signatures providing good discrimination between different classes of objects. To develop a robust target classification method that can be applied to discriminate munitions from non-hostile man-made objects and competing natural clutter, the Matched Subspace Classifier (MSC) framework was adopted in conjunction with multidimensional Acoustic Color (AC) feature data extracted from raw sonar returns.Classification results of the MSC system constructed using two different signal subspace learning methods, namely K-SVD and locality preserving (LP) K-SVD are presented and benchmarked against each other. Additionally, a non-linear version of MSC using the...

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“…[1][2][3][4]. The difficulty of the problem is increased by the low number of data bases of mines available, which makes easy a tendency to specialize any algorithm on one or two databases, without much security that it could work equally well on a new database.…”

Section: ) Classificationmentioning

confidence: 99%

“…1 and in Ref. 2 for the Martin Marietta ACF detection algorithm. Now, this not outstanding performance is obtained with minimal input (two parameters) of the user.…”

Section: Introductionmentioning

confidence: 99%

Mine detection in sonar images with minimal user input

Lisani

Morel²,

Rudin

1999

SPIE Proceedings

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A parameter-free and a priori-information-free preprocessing of sonar images is proposed, which permits a ranking of local extrema in the image according to their likelihood to be a mine-like object. It is shown that an acceptable fully automatic detection algorithm can be built on a variational method which estimates shape information of the possible mines. This algorithm does not use any a prioriinformation on the type of mine or range distance or background type and works without any change on both sonar databases we had available (SonarO and Sonar3) . It therefore can be used as a detection algorithm without any information request the user or designer. Its results could be fed into a classification algorithm like the one proposed in Ref. 1. We also think that the features computed by this variational method could serve for both the detection step and the classification step, thus reducing the number of designer's parameters and opening the way to a parameter-free detection-classification algorithm.

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<title>Sea mine detection and classification using side-looking sonar</title>

Cited by 38 publications

References 0 publications

Analysis of target detection via matrix completion

Analysis of target detection via matrix completion

Underwater UXO classification using Matched Subspace Classifier with synthetic sparse dictionaries

Mine detection in sonar images with minimal user input

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