John T. Sample

Enhancing the US Navy's GIDB Portal with Web Services

Using Web services, the authors have been able to increase the amount of data accessible through the Naval Research Laboratorys geospatial information database (GIDB) portal. They created a Web crawler for Web mapping services (WMS) that harvests and adds data to the portal index and a translator that provides access to non-WMS data through the WMS interface. The integrated Web services broker, using traditional Web services standards, provides automated access to meteorological and oceanographic data

AQS-20 Through-the-Sensor (TTS) performance assessment

Performance of existing and planned mine hunting sensors is dependent on the environment. When the sea floor is a flat smooth hard sandy surface with no mine like clutter on it, then sensor performance is outstanding and acoustic mine hunting is relatively easy. Introduce clutter, a rough seafloor and a soft muddy bottom, sensor performance is seriously degraded making mine hunting operations extremely difficult to impossible. One must know the environment to know sensor performance. Historical environmental data is important but not sufficient. In spite of painstaking efforts to collect, process and disseminate data, historical information is often missing, outdated or in error. To know sensor performance, near realtime environmental data must be collected to verify, supplement and refresh historical holdings. This paper describes the results of two near real-time end-to-end Through-the-Sensor (TTS) demonstrations conducted in FY05 using AQS-20 data. Critical environmental parameters were extracted from the raw tactical data stream using a TTS approach. Data collected by the AQS-20 was processed for bathymetry, sediment type and % burial. Supplemental data was fused with historical information on scene and used to calculate doctrinal bottom type in NAVOCEANOs Bottom Mapping Workstation. The information was passed to MEDAL where track spacing and hunt times were calculated. NAVOCEANO, in a fast reach back mode using TEDServices, examined the data, added value, and returned it. The impact to the mine warfare community is a true sense of sensor performance.

Efficient AKNN spatial network queries using the M-Tree

Aggregate K Nearest Neighbor (AKNN) queries are problematic when performed within spatial networks. While simpler network queries may be solved by a single network traversal search, the AKNN requires a large number costly network distance computations to completely compute results. The M-Tree index, when used with Road Network Embedding, provides an efficient alternative which can return estimates of the AKNN results. The M-Tree index can then be used as a filter for AKNN results by quickly computing a superset of the query results. The final AKNN query results can be computed by sorting the results from the M-Tree. In comparison to Incremental Euclidean Restriction (IER), the M-Tree reduces the overall query processing time and the total number of necessary network distance computations required to complete a query. In addition, the M-Tree filtering method is tunable to allow increasing performance at the expense of accuracy, making it suitable for a wide variety of applications.

Hydraulic Splines: A Hybrid Approach to Modeling River Channel Geometries

The hydraulic spline algorithm generates irregular 2D channel grids from highly accurate cross-sectional survey data at any desired resolution, facilitating its integration with high-density light detection and ranging (lidar) data.

Efficient Approximation of Spatial Network Queries using the M-Tree with Road Network Embedding

Spatial networks, such as road systems, operate differently from normal geospatial systems because objects are constrained to locations on the network. Performing queries on spatial networks demands entirely different solutions. Most spatial queries make use of an R-Tree to process them efficiently. The M-Tree is a data tree index which is capable of indexing data in any metric space. The M-Tree index can replace the R-Tree index for spatial network queries, such as range and KNN queries. The difficulty is that the M-Tree is only as efficient as the distance algorithm used on the underlying objects. Most network distance algorithms, such as A*, are too slow to allow the M-Tree to operate efficiently on spatial networks. The truncated road network embedding (tRNE) maps the network into a higher dimensional space where any LP metric can be used to efficiently compute an accurate approximation of network distance. The M-Tree combined with tRNE creates an efficient index structure for computing spatial network queries. The M-Tree substantially outperforms network expansion, the most popular method of computing spatial network queries, when performing spatial network KNN and range queries.

AQS-20 through-the-sensor environmental data sharing

The Naval Research Laboratory (NRL) has developed an advanced architecture for connecting many maturing Through-The-Sensor (TTS) efforts for an end-to-end demonstration using the AQS-20 mine hunting sensor. The goal of TTS technologies is to exploit tactical sensors to characterize the battlespace environment for Navy Fleet Tactical Decision Aids (TDAs) with minimal impact on tactical systems. The AQS-20 Rapid Transition Process (RTP) will utilize the AQS-20 to demonstrate sensor data collection, processing, fusion, storage, distribution and use in a tactical decision aid. In recent years, NRL has shown that the AQS-20 can be used to obtain swath bathymetry and bottom sediment information in a single flight. In the AQS-20 RTP, these data will be processed and fused with historical databases to provide an improved environmental picture. The RTP will also utilize the Geophysical Data Base Variable resolution (GDBV) dynamic format for storing local datasets. The GDBV dynamic has been developed in prior years to provide an extensible, efficient data storage format for TTS systems. To provide the interconnectivity that is critical to Network Centric Warfare (NCW), the GDBV will be connected to the SPAWAR funded Tactical Environmental Data Services (TEDServices). To complete the flow of information from sensor to user, the RTP will transmit information to the MEDAL TDA through existing connections in The Naval Oceanographic Office’s (NAVOCEANO) Bottom Mapping Workstation (BMW). In addition, TEDServices will handle transmission of the AQS-20 data to NAVOCEANO who serves as the domain authority for oceanographic datasets in the U.S. Navy.

High-frequency acoustic sediment classification in shallow water

A geoacoustic inversion technique for high-frequency (12 kHz) multibeam sonar data is presented as a means to classify the seafloor sediment in shallow water (40-300 m). The inversion makes use of backscattered data at a variety of grazing angles to estimate mean grain size. The need for sediment type and the large amounts of multibeam data being collected with the Naval Oceanographic Offices Simrad EM 121A systems, have fostered the development of algorithms to process the EM 121A acoustic backscatter into maps of sediment type. The APL-UW (Applied Physics Laboratory at the University of Washington) backscattering model is used with simulated annealing to invert for six geoacoustic parameters. For the inversion, three of the parameters are constrained according to empirical correlations with mean grain size, which is introduced as an unconstrained parameter. The four unconstrained (free) parameters are mean grain size, sediment volume interaction, and two seafloor roughness parameters. Acoustic sediment classification is performed in the Onslow Bay region off the coast of North Carolina using data from the 12 kHz Simrad EM 121A multibeam sonar system. Raw hydrophone data is beamformed into 122 beams with a 120-degree swath on the ocean floor, and backscattering strengths are calculated for each beam and for each ping. Ground truth consists of 68 grab samples in the immediate vicinity of the sonar survey, which have been analyzed for mean grain size. Mean grain size from the inversion shows 90% agreement with the ground truth and may be a useful tool for high-frequency acoustic sediment classification in shallow water.

Annotating Uncertainty in Geospatial and Environmental Data

The Geography Markup Language (GML) - the existing standard for encoding geospatial data - has no mechanism for annotating such data with uncertainty. To address this issue while supporting the geospatial communitys existing data and service standards, the authors extend GML to enable uncertainty markup. They demonstrate this extensions use with some common geospatial data types and Web services. The result is a robust capability to share error information while maintaining compatibility with existing geospatial data clients.

Distributed Geospatial Intelligence Integration and Interoperability Through the Gidb® Portal System

This chapter will present the potential benefits from integrated and interoperable geographic data sources. It will discuss the challenges and options involved in creating a geographic portal system and will use the Naval Research Laboratory’s Geospatial Information Database (GIDB©) as an example system.’ The GIDB is the leading tool for integration of geospatial intelligence for homeland security applications. It currently integrates over 600 sources of geospatial intelligence and provides them to users worldwide.

Seismic reflectivity effects from seasonal seafloor temperature variation

The effects of seasonal temperature variation on sound speed contrasts at the seafloor are usually considered negligible in the analysis of seismic data but may be significant at large incidence angles (offsets) important for inversion of sediment elastic properties, or long-range acoustic transmission. In coastal areas, the maximum annual seafloor temperature variation can be several degrees Celsius or more, corresponding to a sound speed variation of 30 m/s or more. Thermal pulses propagate via conduction several meters into the seafloor resulting in a damped quasi-sinusoidal temperature profile with predictable wave number characteristics. The oscillating seasonal and spatial character of this signal creates a time- and frequency-dependent effect on the elastic seafloor reflectivity. Results of numerical simulations show that the expected temperature profile for most sediment types and porosities will have the strongest affect on frequencies between about 60 and 600 Hz, at incidence angles greater than about 50°.