19,928 research outputs found
Investigation of bottom fishing impacts on benthic structure using multibeam sonar, sidescan and video
Bottom fishing gear is known to alter benthic structure, however changes in the shape of the sea floor are often too subtle to be detected by acoustic remote sensing. Nonetheless, long linear features were observed during a recent high-resolution multibeam sonar survey of Jeffreys Ledge, a prominent fishing ground in Gulf of Maine, located about 50 km from Portsmouth, NH. These marks, which have a relief of only few centimeters, are presumed to be caused by bottom dredging gear used in the area for scallop and clam fisheries. The extraction of these small features from a noisy data set (including several instrumental artifacts) presented a number of challenges. To enhance the detection and identification of these features, data artifacts were identified and removed selectively using frequency filtering. Verification was attempted with sidescan sonar and video surveys. While clearly visible on the sidescan sonar records, the bottom marks were not discernable in the video survey. The inability to see the bottom marks with video may be related to the age of the marks, and has important ramifications about appropriate methodologies for quantifying gear impact. Results from multibeam sonar, sidescan sonar and video surveys suggest that the best methodology to deal with inspection of bottom fishing marks is to integrate data in a 3D GIS-like environment
Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs
It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D Power Amplifier, a typical non-linear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of
power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. Firstly, the non-linearity of the sonar transmitter is studied in depth, and the nonlinearity of the power amplifier is modeled and its non-linearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and then the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the non-linear characteristics of the signal. By this way, this method can enhance the communication safety of UASNs
TEXTURAL ANALYSIS AND STATISTICAL INVESTIGATION OF PATTERNS IN SYNTHETIC APERTURE SONAR IMAGES
Textural analysis and statistical investigation of patterns in synthetic aperture sonar (SAS) images is useful for oceanographic purposes such as biological habitat mapping or bottom type identification for offshore construction. Seafloor classification also has many tactical benefits for the U.S. Navy in terms of mine identification and undersea warfare. Common methods of texture analysis rely on statistical moments of image intensity, or more generally, the probability density function of the scene. One of the most common techniques uses Haralick’s Grey Level Co-occurrence Matrix (GLCM) to calculate image features used in the applications listed above. Although widely used, seafloor classification and segmentation are difficult using Haralick features. Typically, these features are calculated at a single scale. Improvements based on the understanding that patterns are multiscale was compared with this baseline, with a goal of improving seafloor classification. Synthetic aperture sonar (SAS) data was provided by the Norwegian Research Defense Establishment for this work, and was labeled into six distinct seafloor classes, with 757 total examples. We analyze the feature importance determined by neighborhood component analysis as a function of scale and direction to determine which spatial scale and azimuthal direction is most informative for good classification performance.Office of Naval Research, Arlington, VA , 22217Lieutenant, United States NavyApproved for public release. Distribution is unlimited
Study for the Sonar Target Pattern Recognition Based on the Acoustic Scattering Features
Study for the Sonar Target Pattern Recognition
Based on the Acoustic Scattering Features
The objects of active sonar are to detect targets and further to acquire their information such as target specification, dimension, motion and state, after processing their echoes. Especially, classification and discrimination of target using active sonar need to utilize the advanced analysis of target echo features.
Specular reflection and scattering from surface irregularities and inner structures contribute to the target echoes. Since contributors of echoes vary with the target kind and target aspect angle, they consititute basic features of the target signal. Feature parameters of the experimental target signal were extracted in three ways which are the envelope in time domain, the time separation pitch in frequency domain, and the short time Fourier transform in time-frequency domain. The extracted features were applied to the pattern recognition techniques to classify and discriminate target.
Classifying and discriminating similarly shaped targets of which dominant component of the echo is specular reflection result in poor assortment and identification. However, the results show better performance when the effect of inner structures appears in target echoes at the specific aspect angle
Machine learning methods for discriminating natural targets in seabed imagery
The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems.
These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation.
Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture
classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world
sonar mosaic imagery.
A number of technical challenges arose and these were
surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation
of pockmark and Sabellaria discrimination is feasible within this framework
Multi-texture image segmentation
Visual perception of images is closely related to the recognition of the different
texture areas within an image. Identifying the boundaries of these regions is an important
step in image analysis and image understanding. This thesis presents supervised and
unsupervised methods which allow an efficient segmentation of the texture regions within
multi-texture images.
The features used by the methods are based on a measure of the fractal dimension
of surfaces in several directions, which allows the transformation of the image into a set
of feature images, however no direct measurement of the fractal dimension is made. Using
this set of features, supervised and unsupervised, statistical processing schemes are
presented which produce low classification error rates. Natural texture images are
examined with particular application to the analysis of sonar images of the seabed.
A number of processes based on fractal models for texture synthesis are also
presented. These are used to produce realistic images of natural textures, again with
particular reference to sonar images of the seabed, and which show the importance of
phase and directionality in our perception of texture. A further extension is shown to give
possible uses for image coding and object identification
Detecting fish aggregations from reef habitats mapped with high resolution side scan sonar imagery
As part of a multibeam and side scan sonar (SSS) benthic survey of the Marine Conservation District (MCD) south of St. Thomas, USVI and the seasonal closed areas in St. Croix—Lang Bank (LB) for red hind (Epinephelus guttatus) and the Mutton Snapper (MS) (Lutjanus analis) area—we extracted signals from water column targets that represent individual
and aggregated fish over various benthic habitats encountered in the SSS imagery. The survey covered a total of 18 km2 throughout the federal jurisdiction fishery management areas. The complementary set of 28 habitat classification digital maps covered a total of 5,462.3 ha;
MCDW (West) accounted for 45% of that area, and MCDE (East) 26%, LB 17%, and MS the remaining 13%. With the exception
of MS, corals and gorgonians on consolidated habitats were significantly more abundant than submerged aquatic vegetation (SAV) on unconsolidated sediments or unconsolidated sediments. Continuous coral habitat was the most abundant consolidated habitat for both MCDW and MCDE (41% and 43% respectively). Consolidated habitats in LB and MS predominantly consisted of gorgonian plain habitat with 95% and 83% respectively. Coral limestone habitat was more abundant than coral patch habitat; it was found near the shelf break in MS, MCDW, and MCDE. Coral limestone and coral patch habitats only covered LB minimally. The high spatial resolution (0.15 m) of the acquired imagery allowed the detection of differing fish aggregation (FA) types. The
largest FA densities were located at MCDW and MCDE over coral communities that occupy up to 70% of the bottom cover.
Counts of unidentified swimming objects (USOs), likely representing individual fish, were similar among locations and occurred primarily over sand and shelf edge areas. Fish aggregation school sizes were significantly smaller at MS than the other three locations (MCDW, MCDE, and LB). This study shows the advantages of utilizing SSS in determining fish distributions and density
Evaluating automatic cross-domain Dutch semantic role annotation
In this paper we present the first corpus where one million Dutch words from a variety of text genres have been annotated with semantic roles. 500K have been completely manually verified and used as training material to automatically label another 500K. All data has been annotated following an adapted version of the PropBank guidelines. The corpus’s rich text type diversity and the availability of manually verified syntactic dependency structures allowed us to experiment with an existing semantic role labeler for Dutch. In order to test the system’s portability across various domains, we experimented with training on individual domains and compared this with training on multiple domains by adding more data. Our results show that training on large data sets is necessary but that including genre-specific training material is also crucial to optimize classification. We observed that a small amount of in-domain training data is already sufficient to improve our semantic role labeler
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