CZMIL is an integrated lidar-imagery system and software suite designed for highly automated generation of physical and environmental information products for coastal zone mapping in the framework of the US Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP). This paper presents the results of CZMIL system validation in turbid water conditions along the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. Results of the USACE May-October 2012 mission in Green Bay, WI and Lake Erie are presented. The system performance tests show that CZMIL successfully achieved 7-8m depth in Mississippi with Kd =0.46m-1 (Kd is the diffuse attenuation coefficient) and up to 41m in Florida when Kd=0.11m-1. Bathymetric accuracy of CZMIL was measured by comparing CZMIL depths with multi-beam sonar data from Cat Island, MS and from off the coast of Fort. Lauderdale, FL. Validation demonstrated that CZMIL meets USACE specifications (two standard deviation, 2σ, ~30 cm). To measure topographic accuracy we made direct comparisons of CZMIL elevations to GPS-surveyed ground control points and vehicle-based lidar scans of topographic surfaces. Results confirmed that CZMIL meets the USACE topographic requirements (2σ, ~15 cm). Upon completion of the Green Bay and Lake Erie mission there were 89 flights with 2231 flightlines. The general hours of aircraft engine time (which doesn't include all transit/ferry flights) was 441 hours with 173 hours of time on survey flightlines. The 4.8 billion (!) laser shots and 38.6 billion digitized waveforms covered over 1025 miles of shoreline.
We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to
form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also
developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion
algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data
at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with
either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification
accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.
Integration of a bathymetric lidar and imaging spectrometer in CHARTS presented the challenge of developing new
algorithms and software for combining these two types of data. To support this development, we conducted several
field campaigns to collect airborne and in-situ data of the water column and seafloor. This work, sponsored by the
Office of Naval Research (ONR) led to development of the Rapid Environmental Assessment (REA) processor. REA
can be used to produce seafloor reflectance images from both sensors, and classification maps of the seafloor.
Ultimately, REA became the prototype software for CZMIL, and the CZMIL Data Processing System (DPS) has been
produced as a continuous refinement of REA. Here, we describe the datasets collected and illustrate results achieved
with the REA software.
KEYWORDS: Cameras, CZMIL, Imaging systems, Calibration, Camera shutters, Digital cameras, CCD cameras, Spatial resolution, LIDAR, Signal to noise ratio
The Coastal Zone Mapping and Imaging Lidar (CZMIL) is a multi-sensor airborne system with dedicated data fusion
software producing 3D images and maps of environmental parameters describing the beach, seafloor and water
column. To reduce overall program development risk, a commercial off-the-shelf (COTS) imaging spectrometer and
digital metric camera are used. These imagers are installed on the same mounting plate as the lidar so as to share
navigation data from a single inertial measurement unit (IMU). In this paper, we discuss the capabilities of the passive
imagers as they relate to spatial and spectral requirements of the U.S. Army Corps of Engineers (USACE) mission,
and illustrate the anticipated data coverage based on the expected deployment mode.
Range measurements in CZMIL1,2 are accomplished with signal processing techniques applied to green lidar waveforms.
In the design phase of the project, we developed software to simulate waveforms for CZMIL, and have used these
simulated waveforms to design ranging algorithms, and test their accuracies. Our results indicate the topographic ranging
accuracy to hard targets should be on the order of 2cm. In this paper, we discuss the simulations, algorithms, and results.
KEYWORDS: Data fusion, Data modeling, Reflectivity, LIDAR, Signal attenuation, Image fusion, CZMIL, Image classification, Double positive medium, 3D modeling
CZMIL will simultaneously acquire lidar and passive spectral data. These data will be fused to produce enhanced
seafloor reflectance images from each sensor, and combined at a higher level to achieve seafloor classification. In the
DPS software, the lidar data will first be processed to solve for depth, attenuation, and reflectance. The depth
measurements will then be used to constrain the spectral optimization of the passive spectral data, and the resulting water
column estimates will be used recursively to improve the estimates of seafloor reflectance from the lidar. Finally, the
resulting seafloor reflectance cube will be combined with texture metrics estimated from the seafloor topography to
produce classifications of the seafloor.
In pursuit of increased reliability and improved signal levels, we employ a scanning approach that supports the high
frequency acquisition requirements of the system while utilizing a large aperture receiver. A continuously rotating
circular scanner was developed using direct inductance drives to rotate a Fresnel prism, acting as synchronized
transmitter-receiver optical element with a fixed incident angle to the sea surface. Circular scanning introduces
challenges in evaluating the system's coverage. By its nature, a circular scanning pattern introduces non-uniformity.
Unlike galvo-based scanners roll and pitch compensation is practically impossible for a large mechanism, therefore
careful planning is required to ensure continuous coverage under realistic operational conditions. Selection of optimal
operation parameters is discussed as well as different ways to evaluate surface coverage in different operation modes.
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