Seabed Detection Using Application Of Image Side Scan Sonar Instrument (Acoustic Signal)

The importance of knowing the method for seabed detection using side-scan sonar images with sonar instrument is a much-needed requirement right now. This kind of threat also requires frequent sonar surveys in such areas. These survey operations need specific procedures and special equipment to ensure survey correctness. In this paper describes the method of observation and retrieval of marine imagery data using an acoustic signal method, to determine a target based on the sea. Side scan sonar is an instrument consisting of single beam transducer on both sides. Side scan sonar (SSS) is a sonar development that is able to show in two-dimensional images of the seabed surface with seawater conditions and target targets simultaneously. The side scan sonar data processing is performed through geometric correction to establish the actual position of the image pixel, which consists of bottom tracking, slant-range correction, layback correction and radiometric correction performed for the backscatter intensity of the digital number assigned to each pixel including the Beam Angle Correction (BAC), Automatic Gain Control (AGC), Time Varied Gain (TVG), and Empirical Gain Normalization (EGN)

Evaluation of a Canonical Image Representation for Sidescan Sonar

Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. As a result, the same physical area can show significant visual differences in SSS images from different survey lines, causing difficulties in tasks such as pixel correspondence and template matching. In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion. The intensity correction includes beam pattern correction and incident angle correction using three different Lambertian laws (cos, cos2, cot), whereas the slant range correction removes the nadir zone and projects the position of SSS elements into equally horizontally spaced, view-point independent bins. The proposed method is evaluated on real data collected by a HUGIN AUV, with manually-annotated pixel correspondence as ground truth reference. Experimental results on patch pairs compare similarity measures and keypoint descriptor matching. The results show that the canonical transformation can improve the patch similarity, as well as SIFT descriptor matching accuracy in different images where the same physical area was ensonified.Comment: 7 pages, 8 figure

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Seabed Characterization through Image Processing of Side Scan Sonar Case Study: Bontang and Batam

Acoustic waves propagate through a medium meet the Snell’s Law, its energy is reflected and some are scattered back at certain angle. The Side Scan Sonar (SSS) methods use this principle to identify seabed character. The intensity of the backscatter greatly depends on the morphology and sediments texture or rocks distributed on seabed.The intensity of backscatter waves is a representation of the morphology, sediments texture, and types of rock that distributed on the seabed, therefore it is possible to estimate sedimentary texture and identify the presence of rocks or coral reefs based on this information. In this publication authors estimate sediments texture, rocks or coral reefs based on backscatter intensity through the image processing on the Side Scan Sonar (SSS) image. Intensity will be converted into pixel values on the image with range value 1-255 (gray scale image) and entropy values which are statistical measures of randomness. Entropy value is maximum when most of pixel value image is in the middle of the colour spectrum range (between very dark to very bright), in contrast, it is minimum when pixel value is in the spectrum of very dark or very bright. Based on both parameters, classification is conducted. The classification is carried out on the SSS image at Bontang and Batam that have very different seabed characters.The classification results using an image processing shows that the distribution of sediment textures consist of 4 (four) classes for either Batam or Bontang. In the Bontang area, very fine sediments were identified which are associated with low value of both intensity and entropy - dark zones in gray scale images, and coarse sediments associated with high value of both intensity and entropy - bright zone in the gray scale image. Similar characteristic is observed in Batam area, which are identified fine sediment (associated to low intensity) - coarse sediments (high intensity). In contrast to Bontang, in Batam the entropy exhibit the opposite value, high value are correlated to fine sediment and vice versa. This might be due to the presence of rocks and sedimentary structures.Keywords: Side Scan Sonar, Intensity, Backscatter and entropy.Gelombang akustik sebagian besar energinya dipantulkan memenuhi prinsip snellius dan sebagian kecil dihamburkan balik dengan sudut. Metode Side Scan Sonar (SSS) memanfaatkan prinsip hambur-balik gelombang untuk mengidentifikasi permukaan dasar laut. Intensitas gelombang dari karakter hambur-balik akan sangat tergantung morfologi dan tekstur sedimen atau batuan dari permukaan dasar lautnya. Intensitas gelombang hambur-balik merupakan representasi dari morfologi, tekstur sedimen, dan jenis batuan yang tersebar di permukaan dasar laut, sehingga sangat memungkinkan untuk melakukan estimasi tekstur sedimen dan identifikasi keberadaan batuan maupun terumbu karang berdasarkan informasi tersebut. Pada publikasi ini akan dilakukan estimasi tekstur sedimen atau batuan berdasarkan intensitas hambur-balik melalui image yang dihasilkan oleh Metode Side Scan Sonar (SSS). Intensitas akan dikonversi ke dalam nilai pixel dalam image dengan rentang nilai 1-255 (gray scale image) dan nilai entropi yang merupakan ukuran statistik ketidakteraturan dari image. Entropi akan maksimum ketika nilai pixel kebanyakan di tengah dari rentang spektrum warna dan sebaliknya akan minimum ketika nilai pixelnya berada di spektrum warna sangat gelap atau sangat terang. Berdasarkan kedua parameter tersebut, kemudian dilakukan klasifikasi. Klasifikasi dilakukan pada data SSS Bontang dan Batam yang memiliki karakter permukaan dasar laut yang sangat berbeda.Hasil klasifikasi dengan image processing memperlihatkan pola sebaran tekstur sedimen masing-masing terdiri dari 4 (empat) kelas baik untuk Batam atau Bontang. Pada area Bontang teridentifikasi sedimen sangat halus yang berasosiasi dengan intensitas dan entropy rendah - zona gelap pada gray scale image dan sedimen kasar yang berasosiasi dengan intensitas dan entropy tinggi - zona terang pada gray scale image. Karakter yang sama juga teramati pada area Batam, yaitu teridentifikasi sedimen halus (berasosiasi dengan intensitas rendah) - sedimen kasar (intensitas tinggi). Namun, berbeda dengan di Bontang, di Batam nilai entropi menunjukkan nilai yang sebaliknya, yaitu nilai tinggi berkorelasi dengan sedimen halus, dan sebaliknya. Hal ini diperkirakan akibat keberadaan batuan dan struktur sedimen.Kata Kunci: Side Scan Sonar, Intensitas, Hambur balik dan Entropi

High-Frequency Volume and Boundary Acoustic Backscatter Fluctuations in Shallow Water

Volume and boundary acoustic backscatter envelope fluctuations are characterized from data collected by the Toroidal Volume Search Sonar (TVSS), a 68 kHz cylindrical array capable of 360° multibeam imaging in the vertical plane perpendicular to its axis. The data are processed to form acoustic backscatter images of the seafloor, sea surface, and horizontal and vertical planes in the volume, which are used to attribute nonhomogeneous spatial distributions of zooplankton, fish, bubbles and bubble clouds, and multiple boundary interactions to the observed backscatter amplitude statistics. Three component Rayleigh mixture probability distribution functions (PDFs) provided the best fit to the empirical distribution functions of seafloor acoustic backscatter. Sea surface and near-surface volume acoustic backscatter PDFsare better described by Rayleigh mixture or log-normal distributions, with the high density portion of the distributions arising from boundary reverberation, and the tails arising from nonhomogeneously distributed scatterers such as bubbles, fish, and zooplankton. PDF fits to the volume and near-surface acoustic backscatter data are poor compared to PDF fits to the boundary backscatter, suggesting that these data may be better described by mixture distributions with component densities from different parametric families. For active sonar target detection, the results demonstrate that threshold detectors which assume Rayleigh distributed envelope fluctuations will experience significantly higher false alarm rates in shallow water environments which are influenced by near-surface microbubbles, aggregations of zooplankton and fish, and boundary reverberation

Occlusion Modeling for Coherent Echo Data Simulation:A Comparison Between Ray-Tracing and Convex-Hull Methods

The ability to simulate realistic coherent datasets for synthetic aperture imaging systems is crucial for the design, development and evaluation of the sensors and their signal processing pipelines, machine learning algorithms and autonomy systems. In the case of synthetic aperture sonar (SAS), collecting experimental data is expensive and it is rarely possible to obtain ground truth of the sensor’s path, the speed of sound in the medium, and the geometry of the imaged scene. Simulating sonar echo data allows signal processing algorithms to be tested with known ground truth, enabling rapid and inexpensive development and evaluation of signal processing algorithms. The de-facto standard for simulating conventional high-frequency (i.e., > 100 kHz) SAS echo data from an arbitrary sensor, path and scene is to use a point-based or facet-based diffraction model. A crucial part of this process is acoustic occlusion modeling. This article describes a SAS simulation pipeline and compares implementations of two occlusion methods; ray-tracing, and a newer approximate method based on finding the convex hull of a transformed point cloud. The full capability of the simulation pipeline is demonstrated using an example scene based on a high-resolution 3D model of the SS Thistlegorm shipwreck which was obtained using photogrammetry. The 3D model spans a volume of 220 × 130 × 25 m and is comprised of over 30 million facets that are decomposed into a cloud of almost 1 billion points. The convex-hull occlusion model was found to result in simulated SAS imagery that is qualitatively indistinguishable from the ray-tracing approach and quantitatively very similar, demonstrating that use of this alternative method has potential to improve speed while retaining high fidelity of simulation.The convex-hull approach was found to be up to 4 times faster in a fair speed comparison with serial and parallel CPU implementations for both methods, with the largest performance increase for wide-beam systems. The fastest occlusion modeling algorithm was found to be GPU-accelerated ray-tracing over the majority of scene scales tested, which was found to be up to 2 times faster than the parallel CPU convex-hull implementation. Although GPU implementations of convex hull algorithms are not currently readily available, future development of GPU-accelerated convex-hull finding could make the new approach much more viable. However, in the meantime, ray-tracing is still preferable, since it has higher accuracy and can leverage existing implementations for high performance computing architectures for better performance

Automatic target recognition in sonar imagery using a cascade of boosted classifiers

This thesis is concerned with the problem of automating the interpretation of data representing the underwater environment retrieved from sensors. This is an important task which potentially allows underwater robots to become completely autonomous, keeping humans out of harm’s way and reducing the operational time and cost of many underwater applications. Typical applications include unexploded ordnance clearance, ship/plane wreck hunting (e.g. Malaysia Airlines flight MH370), and oilfield inspection (e.g. Deepwater Horizon disaster). Two attributes of the processing are crucial if automated interpretation is to be successful. First, computational efficiency is required to allow real-time analysis to be performed on-board robots with limited resources. Second, detection accuracy comparable to human experts is required in order to replace them. Approaches in the open literature do not appear capable of achieving these requirements and this therefore has become the objective of this thesis. This thesis proposes a novel approach capable of recognizing targets in sonar data extremely rapidly with a low number of false alarms. The approach was originally developed for face detection in video, and it is applied to sonar data here for the first time. Aside from the application, the main contribution of this thesis, therefore, is in the way this approach is extended to reduce its training time and improve its detection accuracy. Results obtained on large sets of real sonar data on a variety of challenging terrains are presented to show the discriminative power of the proposed approach. In real field trials, the proposed approach was capable of processing sonar data real-time on-board underwater robots. In direct comparison with human experts, the proposed approach offers 40% reduction in the number of false alarms