A data set for change detection in port environments using sidescan sonar

Transcription

1 Copy No. Defence Research and Development Canada Recherche et développement pour la défense Canada DEFENCE & DÉFENSE A data set for change detection in port environments using sidescan sonar Vincent Myers Defence R&D Canada Atlantic Technical Memorandum DRDC Atlantic TM March 2009

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3 A data set for change detection in port environments using sidescan sonar Vincent Myers Defence R&D Canada Atlantic Technical Memorandum DRDC Atlantic TM March 2009

4 Principal Author Original signed by Vincent Myers Vincent Myers Author Approved by Original signed by David Hopkin David Hopkin Head Maritime Asset Protection Approved for release by Original signed by Ron Kuwahara for C. Hyatt Chair / DRP Her Majesty the Queen in Right of Canada, as represented by the Minister of National Defence, 2009 Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale, 2009

5 Abstract.. Within the context of route survey operations, change detection is the process by which targets are detected by comparing current sonar data with historical surveys. The technique is of particular interest for detecting objects in ports and harbours where debris and a high density of man-made objects can create a prohibitive number of false alarms for traditional methods examining sonar imagery. In order to promote and encourage research into automated change detection algorithms, a database of sidescan sonar images has been produced; the images are taken from two surveys nearly a month apart carried out by the Canadian Navy using the Interim Remote Minehunting and Disposal System (IRMDS) carrying a Klein 5500 sidescan sonar during the winter of Various target signatures were simulated and injected into the second survey using a ray tracing method and placed at specific locations chosen to test different cases normally arising during route survey operations. The images have been processed and georeferenced at different resolutions and the test cases carefully documented in order to provide researchers with data in an easily usable format with which to develop and test algorithms and techniques. Résumé... Dans le contexte des opérations de levé des fonds marins, on peut détecter les mines par la détection des changements en comparant les données sonar actuelles avec celles des levés antérieurs. Cette technique est particulièrement intéressante dans les ports, puisqu à cause de la forte densité de débris et d objets artificiels, on déplore un nombre astronomique de fausses alarmes lors de la chasse aux mines traditionnelle. Pour soutenir la recherche d algorithmes de détection automatisée des changements, nous avons produit une base de données d images obtenues lors de deux levés séparés par un mois en hiver 2008, réalisés avec un sonar à balayage latéral Klein 5500 du Système télécommandé provisoire de la Marine canadienne pour la chasse aux mines et le déminage. Pour que les chercheurs disposent des données dans un format facilement utilisable avec lequel poursuivre leurs études, nous avons traité et géoréférencé les images à différentes résolutions et avons documenté les cas d essai. DRDC Atlantic TM i

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7 Executive summary A data set for change detection in port environments using sidescan sonar Vincent Myers; DRDC Atlantic TM ; Defence R&D Canada Atlantic; March Background: Route survey is the standard Concept of Operations (CONOP) for the Maritime Coastal Defence Vessel (MCDV) s mine countermeasures role. Within the context of route survey operations, change detection is the process by which targets are detected and classified based on comparing current sonar data with historical surveys. The technique is of particular interest for detection of objects in ports and harbours where debris and a high density of manmade objects can create a prohibitive number of false alarms for traditional mine hunting. It is also useful when searching for targets that have an unknown or random form factor, such as underwater improvised explosive devices (IEDs), as well as for environmental monitoring applications. Results: In order to promote and encourage research into automated change detection algorithms, a database of sidescan sonar images was produced; the images are taken from two surveys nearly a month apart carried out by the Interim Remote Minehunting and Disposal System (IRMDS) carrying a Klein 5500 sidescan sonar during the winter of Various target signatures are simulated and injected into the second survey at specific locations to provide new objects on which to test algorithms. The images have been processed and georeferenced at different resolutions and the test cases carefully documented by DRDC in order to provide researchers with data in an easily usable format with which to carry out studies. The data set is one of the few of its kind. Future plans: The data set will be disseminated to groups and organizations studying change detection in academia and private industry and will provide a standard baseline with which to compare results and motivate the development of new technology to help advance the state of change detection technology for sonar imaging sensors in port environments. DRDC Atlantic TM iii

8 Sommaire... Une base de données pour la détection des changements dans les environnements portuaires à l aide de sonars à balayage latéral Vincent Myers; DRDC Atlantic TM ; R & D pour la défense Canada Atlantique; Mars Contexte : Les levés de fonds marins sont un concept d opération normal de la lutte antimines par les navires de défense côtière. Dans le contexte des opérations de levé des fonds marins, on peut détecter les mines par la détection des changements en comparant les données sonar actuelles avec celles des levés antérieurs. Cette technique est particulièrement intéressante dans les ports, puisqu à cause de la forte densité de débris et d objets artificiels, on déplore un nombre astronomique de fausses alarmes lors de la chasse aux mines traditionnelle. Elle pourrait être également utile lors de la recherche d objectifs aux formes inconnues ou aléatoires, notamment la chasse aux dispositifs explosifs de circonstance et la surveillance environnementale. Résultats : Pour soutenir la recherche d algorithmes de détection automatisée des changements, nous avons produit une base de données d images obtenues lors de deux levés séparés par un mois en hiver 2008, réalisés avec un sonar à balayage latéral Klein 5500 du Système télécommandé provisoire de chasse aux mines et de déminage. Pour obtenir de nouveaux objets sur lesquels éprouver les algorithmes expérimentaux, nous avons simulé différentes signatures d objectif que nous avons intégrées à des endroits particuliers du second levé. Pour que les chercheurs disposent des données dans un format facilement utilisable avec lequel poursuivre leurs études, nous avons traité et géoréférencé les images à différentes résolutions et avons documenté les cas d essai. Cet ensemble est l une des rares collections de données de ce genre. Futures recherches : Les données seront distribuées aux groupes et organisations universitaires et industriels qui étudient la détection des changements. Elles constituent une référence normalisée avec laquelle on pourra comparer les résultats. Elles encourageront la mise au point de nouvelles technologies qui contribueront aux progrès technologiques des capteurs sonar imageants dans les environnements portuaires. iv DRDC Atlantic TM

9 Table of contents Abstract... i Résumé... i Executive summary... iii Sommaire... iv Table of contents... v List of figures... vi List of tables... vii 1 Introduction Sonar data Surveys Georeferencing Overlap & Navigation Accuracy Target Generation Image artefacts Super-resolution images File format and precision Test cases Test case Test case Test case Test case Test case Conclusion References Distribution list DRDC Atlantic TM v

10 List of figures Figure 1 Survey pattern for Jan 30 th and Feb 18 th... 3 Figure 2 Some examples of image artefacts. (a) the wake of a propeller being imaged by the sonar. (b) shows a fanning effect which occurs during turns and (c) a navigation drop out which causes the navigation smoothing method to place pings where there are missing data, thus causing the georeferencing algorithm to improperly geolocate data... 6 Figure 3 Examples of super-resolution geocoding. The images on the left provide some greater level of detail, in addition to greater pixel numbers, than the images on the right Figure 4 Test case 1 cylindrical target. The target is seen on two files from two angles (a) and (b) which are 180 o from each other. The scenario is repeated for cone and wedge shapes. The long scour, attached line and crab pots (white circular objects) can be used to reduce the ambiguity of this target... 9 Figure 5 Test case 2.1 for cone-shaped target. The target is placed at three positions which vary the proximity to the other real targets in the immediate vicinity Figure 6 Test case 2.2 concerns targets in proximity to distinctive pattern of objects. Such patterns are usually used by human operators to detect changes and provide context to detections Figure 7 Test case 3: A highly cluttered seabed within a turn and several embedded targets Figure 8 Test case 4 a cylinder is simulated at varying ranges to test performance versus distance perpendicular to the ship track Figure 9 Test case 5 is a cylinder near a rocky outcrop to provide context vi DRDC Atlantic TM

11 List of tables Table 1 Test case 1 details... 8 Table 2 Test case 2.1 details Table 3 Test case 2.2 details Table 4 Test case 3 details Table 5 Test case 4 details Table 6 Test case 5 details DRDC Atlantic TM vii

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13 1 Introduction Change detection within the context of route survey operations with side-looking sonars is performed by comparing current sonar data with historical data used as a baseline. Objects that are present in the new survey which were not found in the baseline survey are considered suspicious and to be investigated. The technique is particularly useful in cluttered environments such as ports and harbours where the level of clutter (here defined as the density of non targets with target-like signatures) and man-made debris is very high. As opposed to traditional techniques, the change detection tactic generally requires control of the area which is to be employed and carries the additional overhead in managing and maintaining a database of historical data and establishing a clean baseline. However, it does provide the advantage of being able to detect targets when: 1. the size, shape and nature of the threat is not known in advance (such as is the case of underwater improvised explosive devices (IEDs)) and, 2. in areas of very high clutter where the expected false-alarm rate for traditional minehunting is prohibitively high. The change detection technique allows one to automatically discount target-like signatures which are already known to be benign, thus reducing the time spent prosecuting false alarms. While in minehunting operations one can generally attempt to avoid areas of high clutter, it is often not possible in one s own port due to chokepoints, traffic division schemes and Q-route placements which sometimes force vessels to sail into areas of high clutter and high minehunting difficulty. In such cases, change detection is often the only viable method for reducing risk to follow-on traffic to an acceptable level. Change detection assumes that 1. a historical database exists and is maintained. This is often difficult in expeditionary operations since a reasonable control of the area is required. In some cases, covert operations could be undertaken to establish a historical baseline. 2. the seafloor does not change significantly between surveys. Depending on many factors sedimentation can occur very quickly, changing the underwater landscape and objects can become buried and unburied over a very short time period. The successful application of change detection requires that it be possible to obtain and keep a stable baseline of the seafloor with which to compare the survey at hand. This will dictate the frequency of the route survey operations but in some cases it may be nearly impossible and not cost-effective. The change detection operations with sidescan sonar may be done in a manual, semi-automated or fully-automated way. For instance, a simple method is to query the database in order to obtain all contacts within a given distance of a newly detected contact. If any results are returned, then the new contact is considered historical and benign. If the area is highly cluttered, then many ambiguous contacts could be returned. In this case, contextual information (a landmark on the seafloor) or object characteristics (object shadow shape and size) can be useful in filtering out DRDC Atlantic TM

14 false alarms. Due to the geometry of sonar, the ensonification aspect can also cause ambiguities in data / target association between missions, or even within the same mission. Some methods have been developed which will warp or transform constellations of contacts onto one another to compensate for navigation inaccuracies [1]. A semi-automated way to help a human operator is to cause two co-located images to rapidly flicker between each other in order to help located new objects [2]. Research at DRDC is focussing in part on developing semi and fully automated change detection methods for use in cluttered environments. In order to facilitate this process, a data set of sidescan sonar images has been gathered and processed in order to help DRDC, university, industry and other government departments to exchange results on a common data set. The data set was gathered with the Interim Remote Minehunting and Disposal System (IRMDS) using a Klein 5500 sidescan sonar operating at a center frequency of 455 khz with a bandwidth of 20 khz. The images are beamformed to create five equally spaced beams at a range of 75 meters each side. The nominal resolution of the images is meters in range and 0.10 to 0.20 meters in azimuth, depending on range [3]. The data consist of two surveys, one obtained on January 30 th, 2008 and a second over the same area on February 18 th, The two surveys cover roughly the same area in the same direction (thus providing consistent illumination of objects on the seafloor) and consist of a standard survey pattern within a harbour followed by a long route leading out. The acoustic data were geo-located at two different resolutions to investigate the effect of this parameter on the performance of an automated system. Details on the data set, processing steps and target generation method are given in Section 2. Target signatures were simulated and injected in certain locations of the second survey in order to provide controlled test cases; these are described in detail in Section 3. Some administrative details on the use and control of these data are provided in Section 4. 2 DRDC Atlantic TM

15 2 Sonar data This section gives a high-level overview of the acoustic data and the survey patterns contained in the database. The acoustic data were gathered over two separate days (30 January and 18 February) in the winter of 2008 in a Canadian port. The database contains 48 files from the first survey and 52 files from the second survey. Each file consists of roughly one minute of data. 2.1 Surveys The survey pattern consists of three or four tracks in a roughly east-west direction followed by two long tracks in a roughly north-south direction. The east-west tracks are within the port installation, and thus contain the majority of clutter and debris. The north-south tracks are outside of the port with a seabed that is less cluttered, and lead out to the open sea; they do, however, contain the occasional man-made object such as crab traps, and distinctive naturally occurring seabed features which give context to the imagery positions. The tracks of the vehicle are shown in Figure 1; it can be seen that although the routes for the two different days were not exactly the same, there is significant overlap between the two. When the tracks overlap, the mission survey lines are nearly identical and due to the geometry of sidescan sonar, the illumination of the targets will be nearly the same, greatly reducing the complexity of the change detection problem for this data set. Figure 1 Survey pattern for Jan 30 th and Feb 18 th DRDC Atlantic TM

16 2.2 Georeferencing Georeferencing of the Klein sonar data is performed using the algorithm described in [4]. Navigation and telemetry data are smoothed using a 20 point boxcar filter for each file of sonar data. These include position, heading and altitude and allow for better positioning of acoustic pings, particularly when some sensors provide data less frequently than the ping rate (e.g. a GPS). Images are also normalized using a simple technique to remove effects of beam pattern, grazing angle and propagation loss and create an image which is constant (in amplitude) with lateral range. Finally, images are georeferenced on a grid with a resolution of 0.11m x 0.11m for the normal images and 0.04 m x 0.04m for the super resolution images (described below). Files are saved in GeoTIFF format [5] with an accompanying tiff world file which locates the image on an flat grid. Conversions of latitudes and longitudes were performed using the Universal Transverse Mercator (UTM) coordinate system and the WGS84 projection. An offset x/y coordinate was subtracted from the positions so that the files are at a position around [0, 0]. 2.3 Overlap & Navigation Accuracy Missions are designed to provide some overlap in order to cover each area more than once. In addition, each survey overlaps with the other one. Therefore each grid position may be covered zero, one, two or more times on each survey. The files that overlap for which change detection test cases have been generated are listed in Section 3. Although the vehicle can be positioned to the accuracy of the DGPS used in the IRMDS, the towfish, which is the reference point of the sonar data, cannot. No acoustic positioning was used during these missions; rather, a straight line cable model based on simple geometry and the amount of cable scope is used to predict the position of the sonar. Previous work has estimated the accuracy of this method to be approximately 5-8 meters RMS [6]. 2.4 Target Generation The data from these missions were gathered during regular route survey operations. they therefore provide a realistic data set to test change detection algorithms. However, no target objects were purposefully placed on the seabed in order to scientifically determine the performance of change detection methods. In order to determine the ability to detect changes on for targets of interest, target signatures were simulated to provide controlled test cases (Section 3). The target signatures were simulated using the method described in [7]. There are three types of targets: a large cylinder (~ 2 m length x 0.5 m height), a truncated cone (1 m base, 0.5 m height) shape and a wedge shaped object (~ 1 m base, 0.4 m height). An orientation in the world coordinates is chosen and the signature is generated at the appropriate orientation depending on the heading of the sonar track. When a location is chosen at which to place a target signature, it is first determined if this location is also seen by another leg during the same survey. This will cause the target to be ensonnified (and required to be simulated) in another image. In order to keep the positioning accuracy realistic, a landmark which is seen in both images is chosen as the point from which an offset will be applied. For instance, in Test Case 1, the crab trap at WSW 4 DRDC Atlantic TM

17 location from the target position (the white circle seen in Figure 1) was chosen and for each overlapping image, the target was simulated at a point Δx Δy from the crab pot, which represent different positions in eastings/northings on the UTM grid, but are consistent with each other. Such a method provides realistic target locations without the need for modelling the accuracy of the towfish positioning method. 2.5 Image artefacts Sonar images can contain artefacts that present challenges to any automated processing method. Some are due to the processing used to produce georeferenced images, others to the complex environment or the intermittent failures of telemetry sensors. Therefore it is conceivable that these could be interpreted as changes since the artefacts are unlikely to occur in the same location for each survey (ship wakes, for instance). Examples of such artefacts are shown in Figure 2. For instance: Ship wakes: The IRMDS system is usually accompanied by a RHIB chase boat to manage water space. If the vehicle crosses over the wake of its escort RHIB, the wake will be imaged by the sonar, due to the wide vertical beam pattern. Errors in telemetry data: A telemetry sensor may occasionally cut out and briefly fail to report position or information, thus causing the georeferencing algorithm to geo-locate pixels in incorrect locations. Care was taken to remove the largest periods of bad telemetry data, although the data set still contains some cases of short sensor failures. Poor sensor performance: The combination of the challenging acoustic propagation environment that occurs in shallow ports, combined with the particular design of this sidescan sonar, will cause a variable sensor performance which is a function of lateral range to the sonar array. At far ranges, the signal-to-noise ratio can be low enough as to cause an amplification of noise; this occurs throughout these two survey missions. In addition, the first 10 meters of data have been automatically removed due to poor sensor performance, resulting in a black stripe down the middle of the survey track. Geometric effects: These occur typically during turns, as the sonar is designed to provide full coverage for straight survey lines at maximum tow speed. Depending on the turning radius, a fanning effect can happen to the beams on the outer part of the turn, while a converging effect can occur for the beams on the inner part of the turn. An example is given in Figure 2(b). 2.6 Super-resolution images As with most sidescan sonars, the Klein sonar has different resolutions in range and along-track. Typically, when georeferencing images, the largest (poorest) resolution is chosen as the common resolution for the georeferenced grid, plus some small percentage to avoid aliasing effects (in this case 0.11 x 0.11 meters, which is the along-track resolution (0.1) plus a default of 10 percent). However, one of the most important factors in the performance of image-based methods is the resolution of the image; a greater image resolution can provide better information and higher statistical precision to detect small changes, such as a mine-like target. Of course, simply DRDC Atlantic TM

18 interpolating data will not provide any new information [8], however, since the resolution is in fact better in the across-track direction for this sensor, an attempt was made at interpolating the worse along-track resolution to match the better across-track resolution and geocoding at this new resolution (in this case 0.04 x 0.04 meters). The result is an image that preserves greater detail than the original georeferenced image as well as a greater number of pixels for detecting smaller changes. Some close-up examples of objects which are geocoded to super-resolution are shown in Figure File format and precision For each sonar data file, two GeoTIFFs are created: 1. An unsigned 8-bit tiff file with a dynamic range that has been adjusted from such that it can easily be displayed and visualized by most image processing software; and 2. a 32 bit floating-point TIFF image which preserves the original georeferenced data. For the floating point image, no data is represented by , while for the 8-bit tiff, no data is assigned the value 0, which is also a valid sonar value; therefore an ambiguous situation where no data can be confused with a very low sonar return can occur for the 8-bit images. All files are the same size: 2701x2701 pixels for the normal resolution images and 8101 x 8101 pixels for the super-resolution images. (a) (b) (c) Figure 2 Some examples of image artefacts. (a) the wake of a propeller being imaged by the sonar. (b) shows a fanning effect which occurs during turns and (c) a navigation drop out which causes the navigation smoothing method to place pings where there are missing data, thus causing the georeferencing algorithm to improperly geolocate data. 6 DRDC Atlantic TM

19 Figure 3 Examples of super-resolution geocoding. The images on the left provide some greater level of detail, in addition to greater pixel numbers, than the images on the right. DRDC Atlantic TM

20 3 Test cases This section lists the five test cases included in this data set. Each test case has a specific rationale which is explained in each subsection. 3.1 Test case 1 The first test case is intended to test the ability to detect new objects in relatively benign surroundings. In traditional MCM, this is a less challenging scenario. However, similar to landmarked-based navigation, this case is more difficult for change detection techniques which require control points or constellations to provide context to the changes. Figure 4 shows one of the simulated targets and some of its surrounding area. Table 1 Test case 1 details Number of targets per file 1 Number of test targets (separate files) 3 Target 1 Target 2 Target 3 Number of overlapping files in change detection survey 2 Filename Cylinder Cone Wedge _22_48_02_338_UTC Position (Δ easting, Δ northing ) (Image ) , (589, 1059) Filename _22_28_58_197_UTC Position (Δ easting, Δ northing ) (Image ) , (1024, 1797) Total number of files for Test Case 1 6 Number of files that overlap from the first survey 2 Filenames _19_40_36_455_UTC _19_28_58_815_UTC 8 DRDC Atlantic TM

21 (a) (b) Figure 4 Test case 1 cylindrical target. The target is seen on two files from two angles (a) and (b) which are 180 o from each other. The scenario is repeated for cone and wedge shapes. The long scour, attached line and crab pots (white circular objects) can be used to reduce the ambiguity of this target. 3.2 Test case 2 The aim of test case 2 is to determine the effectiveness of change detection tactics in areas containing a large number of signatures similar to those of the targets of interest. It is subdivided into test cases 2.1 and 2.2. The former tests the performance versus the proximity to another target by systematically varying the distance to another target. The latter places it near a constellation of targets. Figure 5 and Figure 6 show images of the target test cases. DRDC Atlantic TM

22 Table 2 Test case 2.1 details Number of targets per file 1 Number of test targets (separate files) 3 Target 1 Target 2 Target 3 Number of overlapping files in change detection survey 2 Filename Cone Cone Cone _22_09_37_303_UTC Target1 position (Δ easting, Δ northing ) (Image ) , (807, 1875) Target2 position (Δ easting, Δ northing ) (Image ) , (867, 1903) Target3 position (Δ easting, Δ northing ) (Image ) , (914, 1931) Filename _22_15_44_622_UTC Target1 position (Δ easting, Δ northing ) (Image ) , (1553, 1659) Target2 position (Δ easting, Δ northing ) (Image ) , (1604, 1686) Target3 position (Δ easting, Δ northing ) (Image ) , (1650, 1714) Total number of files for Test Case Number of files that overlap from the first survey 2 Filenames _18_56_36_740_UTC _18_50_12_425_UTC Table 3 Test case 2.2 details Number of targets per file 1 Number of test targets (separate files) 2 Target 1 Target 2 Number of overlapping files in change detection survey 1 Filename Cylinder Cone _22_15_44_622_UTC Position (Δ easting, Δ northing ) (Image ) , (1966, 2187) Total number of files for Test Case Number of files which overlap in first survey 1 Filenames _18_55_44_429_UTC 10 DRDC Atlantic TM

23 (a) (b) (c) Figure 5 Test case 2.1 for cone-shaped target. The target is placed at three positions which vary the proximity to the other real targets in the immediate vicinity. (a) (b) Figure 6 Test case 2.2 concerns targets in proximity to distinctive pattern of objects. Such patterns are usually used by human operators to detect changes and provide context to detections. DRDC Atlantic TM

24 3.3 Test case 3 Test case 3 is intended to measure the performance of algorithms, with the sonar operating within a turn and in highly cluttered areas. Four cone targets have been placed a different locations within the turn, on both the port and starboard sides. A great deal of clutter of roughly the same size and shape as the cones are already present, in addition to some distinct structures on the seabed. Most of the image is shown in Figure 7. Table 4 Test case 3 details Number of targets per file 4 Number of test targets (separate files) 1 Number of overlapping files in change detection survey 1 Filename Target Type _22_12_41_511_UTC Cone Position (Δ easting, Δ northing ) (Image ) , (1710, 724) Target Type Cone Position (Δ easting, Δ northing ) (Image ) , (1530, 711) Target Type Cone Position (Δ easting, Δ northing ) (Image ) , (1383, 991) Target Type Cone Position (Δ easting, Δ northing ) (Image ) , (935, 838) Total number of files for Test Case 3 1 Number of files that overlap from the first survey Filenames 2 (Note: the two files are sequential and are from the same track) _18_52_47_043_UTC _18_53_46_427_UTC 12 DRDC Atlantic TM

25 Figure 7 Test case 3: A highly cluttered seabed within a turn and several embedded targets. 3.4 Test case 4 As mentioned above, the performance of sidescan sonar varies with range, and this performance is important when planning survey missions to ensure coverage of an area. The purpose of test case 4 is to allow one to determine how much this variability affects the performance of change detection. To this end, the same target (a cylinder) has been simulated at 5 different ranges from the sensor. All 5 targets are shown in a single image in Figure 8. DRDC Atlantic TM

26 Table 5 Test case 4 details. Number of targets per file 1 Number of test targets (separate files) 5 Number of overlapping files in change detection survey 1 Filename Target _22_45_27_761_UTC Cylinder Position 1 (Δ easting, Δ northing ) (Image ) , (1054, 1114) Position 2 (Δ easting, Δ northing ) (Image ) , (1143, 1138) Position 3 (Δ easting, Δ northing ) (Image ) , (1239, 1182) Position 4 (Δ easting, Δ northing ) (Image ) , (1361, 1188) Position 5 (Δ easting, Δ northing ) (Image ) , (1483, 1218) Total number of files for Test Case 4 5 Number of files that overlap from the first survey 1 Filenames _19_37_51_701_UTC Figure 8 Test case 4 a cylinder is simulated at varying ranges to test performance versus distance perpendicular to the ship track. 14 DRDC Atlantic TM

27 3.5 Test case 5 Test case 5 is intended to validate the use of seabed features as contextual information to aid in detecting changes between surveys. Targets were simulated near some seabed features which appear to be an outcrop of the seabed, which creates a distinct pattern. It is shown in Figure 9. Table 6 Test case 5 details Number of targets per file 1 Number of test targets (separate files) 2 Target 1 Target 2 Number of overlapping files in change detection survey 1 Filename Cylinder *(note that the simulation of the cylinder is not completely accurate, as the shadow would likely be blocked by the unusual seabed bathymetry. This is an artefact of the simulation method used.) Wedge _22_10_38_396_UTC Position 1 (Δ easting, Δ northing ) (Image ) , (1627, 1505) Total number of files for Test Case 5 2 Number of files which overlap in first survey 1 Filenames _18_51_54_855_UTC Figure 9 Test case 5 is a cylinder near a rocky outcrop to provide context. DRDC Atlantic TM

28 4 Conclusion This document has described a data set of sidescan sonar images that were gathered, processed and have had target signatures embedded into them, for the purpose of exploring the use of change detection techniques in a port environment. It is intended to encourage research and development into improving and extending the current state of the art in change detection methods. The data described in this document are subject to release approval and restrictions on dissemination. Interested individuals and organizations should contact the following in order to obtain a copy of the data set: Head / Maritime Asset Protection Defence R&D Canada Atlantic PO Box 1012, Dartmouth Nova Scotia, Canada, B2Y 3Z7 atl.library@drdc-rddc.gc.ca +001 (902) Authors who publish results using this data set are asked to acknowledge DRDC Atlantic as the source of these data. Those wishing to refer to this data set should cite this document. 16 DRDC Atlantic TM

29 References... [1] Coiras, E., Baralli, F., and Evans, B., Rigid data association for shallow water surveys, IET Radar, Sonar and Navigation, Vol. 1(5), pp , [2] Poeckert, R.H., Change detection using blink comparison of route survey sonar imagery, Defence Research Establishment Pacific, December, [3] Key, W.H., Side scan sonar technology, OCEANS 2000 MTS/IEEE Conference Proceedings, pp , [4] Fawcett, J.A, Bolt, L., Myers, V., and Crawford, A., Processing data with the Defence Research Establishment Atlantic sidescan sonar image processing system, Shallow Survey 2001 Conference Proceedings, [5] Ritter, N. and Ruth, M. GeoTIFF format specification, ( [6] Crawford, A., Myers, V. and Hopkin, D. Target positioning accuracy analysis using georeferenced sidescan sonar data, RMS TDP Builds 2 and 3, DRDC Atlantic TM , [7] Myers, V., Davies, G. and Bryan, K., A hybrid approach to the evaluation of high-resolution sonar minehunting operations, Military Operations Research, 2008, (under review) [8] Cover, T. and Thomas, J., Elements of Information Theory, Wiley, DRDC Atlantic TM

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31 Distribution list Document No.: DRDC Atlantic TM LIST PART 1: Internal Distribution by Centre 1 Vincent Myers (author) 1 Anna Crawford 1 John Fawcett 1 David Hopkin 5 Library 9 TOTAL LIST PART 1 LIST PART 2: External Distribution by DRDKIM 1 Library & Archives Canada (Atten: Military Archivist, Government Records Branch) 1 DRDKIM 2 TOTAL LIST PART 2 11 TOTAL COPIES REQUIRED DRDC Atlantic TM

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33 DOCUMENT CONTROL DATA (Security classification of title, body of abstract and indexing annotation must be entered when the overall document is classified) 1. ORIGINATOR (The name and address of the organization preparing the document. Organizations for whom the document was prepared, e.g. Centre sponsoring a contractor's report, or tasking agency, are entered in section 8.) 2. SECURITY CLASSIFICATION (Overall security classification of the document including special warning terms if applicable.) Defence R&D Canada Atlantic 9 Grove Street P.O. Box 1012 Dartmouth, Nova Scotia B2Y 3Z7 UNCLASSIFIED 3. TITLE (The complete document title as indicated on the title page. Its classification should be indicated by the appropriate abbreviation (S, C or U) in parentheses after the title.) A data set for change detection in port environments using sidescan sonar 4. AUTHORS (last name, followed by initials ranks, titles, etc. not to be used) Myers, V. 5. DATE OF PUBLICATION (Month and year of publication of document.) March a. NO. OF PAGES (Total containing information, including Annexes, Appendices, etc.) 32 6b. NO. OF REFS (Total cited in document.) 8 7. DESCRIPTIVE NOTES (The category of the document, e.g. technical report, technical note or memorandum. If appropriate, enter the type of report, e.g. interim, progress, summary, annual or final. Give the inclusive dates when a specific reporting period is covered.) Technical Memorandum 8. SPONSORING ACTIVITY (The name of the department project office or laboratory sponsoring the research and development include address.) Defence R&D Canada Atlantic 9 Grove Street P.O. Box 1012 Dartmouth, Nova Scotia B2Y 3Z7 9a. PROJECT OR GRANT NO. (If appropriate, the applicable research and development project or grant number under which the document was written. Please specify whether project or grant.) 9b. CONTRACT NO. (If appropriate, the applicable number under which the document was written.) 11cf 10a. ORIGINATOR'S DOCUMENT NUMBER (The official document number by which the document is identified by the originating activity. This number must be unique to this document.) 10b. OTHER DOCUMENT NO(s). (Any other numbers which may be assigned this document either by the originator or by the sponsor.) DRDC Atlantic TM DOCUMENT AVAILABILITY (Any limitations on further dissemination of the document, other than those imposed by security classification.) Unlimited 12. DOCUMENT ANNOUNCEMENT (Any limitation to the bibliographic announcement of this document. This will normally correspond to the Document Availability (11). However, where further distribution (beyond the audience specified in (11) is possible, a wider announcement audience may be selected.)) Unlimited

34 13. ABSTRACT T (A brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is highly desirable that the abstract of classified documents be unclassified. Each paragraph of the abstract shall begin with an indication of the security classification of the information in the paragraph (unless the document itself is unclassified) represented as (S), (C), (R), or (U). It is not necessary to include here abstracts in both official languages unless the text is bilingual.) Within the context of route survey operations, change detection is the process by which targets are detected and classified by comparing current sonar data with historical surveys. The technique is of particular interest for detecting objects in ports and harbours where debris and a high density of man-made objects can create a prohibitive number of false alarms for traditional methods examining sonar imagery. In order to promote and encourage research into automated change detection algorithms, a database of sidescan sonar images has been produced; the images are taken from two surveys nearly a month apart carried out by the Canadian Navy using the Interim Remote Minehunting and Disposal System (IRMDS) carrying a Klein 5500 sidescan sonar during the winter of Various target signatures were simulated and injected into the second survey using a ray tracing method and placed at specific locations chosen to test different cases normally arising during route survey operations. The images have been processed and georeferenced at different resolutions and the test cases carefully documented in order to provide researchers with data in an easily usable format with which to develop and test algorithms and techniques. 14. KEYWORDS, DESCRIPTORS or IDENTIFIERS (Technically meaningful terms or short phrases that characterize a document and could be helpful in cataloguing the document. They should be selected so that no security classification is required. Identifiers, such as equipment model designation, trade name, military project code name, geographic location may also be included. If possible keywords should be selected from a published thesaurus, e.g. Thesaurus of Engineering and Scientific Terms (TEST) and that thesaurus identified. If it is not possible to select indexing terms which are Unclassified, the classification of each should be indicated as with the title.) Change Detection, Sidescan Sonar, ATR

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