Airborne light detection and ranging (LIDAR) technology now makes it possible to sample the Earth's surface with point spacings well below 1 m. It is, however, time consuming, costly, and technically challenging to directly use very high resolution LIDAR data for hydraulic modeling because of the computational requirements associated with solving fluid dynamics equations over complex boundary conditions in large data sets. For high relief terrain and urban areas, using coarse digital elevation models (DEMs) can cause significant degradation in hydraulic modeling, particularly when artificial obstructions, such as buildings, mask spatial correlations between terrain points. In this paper we present a strategy to reduce the computational complexity in the estimation of surface water discharge through a decomposition of the DEM data, wherein features have different characteristic spatial frequencies. Though the optimal DEM scale for a particular application will ultimately be decided by the user's tolerance for error, we present guidelines to choose a proper scale by balancing computer memory usage and accuracy. We also suggest a method to parameterize man-made structures, such as buildings in hydraulic modeling, to efficiently and accurately account for their effects on surface water discharge.
In the last decade, various algorithms have been developed for extracting the digital terrain model from LiDAR point clouds. Although most filters perform well in flat and uncomplicated landscapes, landscapes containing steep slopes and discontinuities are still problematic. In this research, we develop a novel bare-earth extraction algorithm consisting of segmentation modeling and surface modeling based on our previous work, forest canopy removal. The proposed segmentation modeling is built on a triangulated irregular network and composed of triangle assimilation, edge clustering, and point classification to achieve better discrimination of objects and preserve terrain discontinuities. The surface modeling is proposed to iteratively correct both Type I and Type II errors through estimating roughness of digital surface/terrain models, detecting bridges and sharp ridges, etc. Finally, we have compared our obtained filtering results with twelve other filters working on the same fifteen study sites provided by the ISPRS. Our average error and kappa index of agreement in the automated process are 4.6% and 84.5%, respectively, which outperform all other twelve proposed filters. Our kappa index, 84.5%, can be interpreted as almost perfect agreement. In addition, applying this work with optimized parameters further improves performance.
An approach to simulate synthetic aperture sonar (SAS) images with known autocorrelation functions (ACF)
and single-point statistics is presented. ACF models for generating textures with and without periodicities are
defined and explained. Simulated textures of these models are compared visually with real SAS image textures.
Distortion and degradation of the synthetic textures are examined for various simulation parameter choices.
The thermal imaging cameras can see the heat signature of people, boats, and vehicles in total darkness as well as
through smoke, haze, and light fog, but not through the forest canopy. This study develops a novel algorithm to help
detecting obscure targets underneath forest canopy and mitigate the vegetation problem for those bare ground point
extraction filters as well. By examining our automatically processed results with actual LiDAR data, the forest canopy is
successfully removed where all obscure vehicles or buildings underneath canopy can now be easily seen. Besides, the
occluded rate of forest canopy and the detailed underneath x-y point distribution can be easily obtained accordingly. This
will be very useful for predicting the performance of occluded target detection with respect to various object locations.
Single-point statistical properties of envelope-detected data such as signal returns from synthetic aperture radar
and sonar have traditionally been modeled via the Rayleigh distribution and more recently by the K-distribution.
Two-dimensional correlations that occur in textured non-Gaussian imagery are more difficult to model and
estimate than Gaussian textures due to the nonlinear transformations of the time series data that occur during
envelope detection. In this research, textured sonar imagery is modeled by a correlated K-distribution. The
correlated K-distribution is explained via the compound representation of the one-dimensional K-distribution
probability density function. After demonstrating the model utility using synthetically generated imagery, model
parameters are estimated from a set of textured sonar images using a nonlinear least-squares fit algorithm. Results
are discussed with regard to texture segmentation applications.
High-resolution sonar images of the sea floor contain rich spatial information that varies widely depending on
survey location, sea state, and sensor platform-induced artifacts. Automatically segmenting sonar images into
labeled regions can have several useful applications such as creating high-resolution bottom maps and adapting
automatic target recognition schemes to perform optimally given the measured environment. This paper presents
a method for sonar image segmentation using graphical models known as dynamic trees (DTs). A DT is a mixture
of simply-connected tree-structured Bayesian networks (TSBNs), a hierarchical two-dimensional Bayesian
network, where the leaf node states of each TSBN are the label of each image pixel. The DT segmentation
task is to find the best TSBN mixture that represents the underlying data. A novel use of the K-distribution
as a likelihood function for associating sonar image pixels with the appropriate bottom-type label is introduced.
A simulated annealing stochastic search method is used to determine the maximum a posteriori (MAP) DT
quadtree structure for each sonar image. Segmentation results from several images are presented and discussed.
KEYWORDS: LIDAR, Data modeling, Motion measurement, Data centers, Data analysis, Motion models, Error analysis, Laser scanners, 3D scanning, Vegetation
Land surface elevation measurements from airborne laser swath mapping (ALSM) data can be irregularly spaced due to occlusion by forest canopy or scanner and aircraft motion. The measurements are usually interpolated into a regularly spaced grid using techniques such as Kriging or spline-interpolation. In this paper a probabilistic
graphical model called a Bayesian network (BN) is employed to interpolate missing data. A grid of nodes is imposed over ALSM measurements and the elevation information at each node is estimated using two methods: 1) a simple causal method, similar to a Markov mesh random field (MMRF), and 2) BN belief propagation. The interpolated results of both algorithms using the maximum a posteriori (MAP) estimates are presented and compared. Finally, uncertainty measures are introduced and evaluated against the final estimates from the BN belief propagation algorithm.
Commercially marketed airborne laser swath mapping (ALSM) instruments currently use laser rangers with sufficient energy per pulse to work with return signals of thousands of photons per shot. The resulting high signal to noise level virtually eliminates spurious range values caused by noise, such as background solar radiation and sensor thermal noise. However, the high signal level approach requires laser repetition rates of hundreds of thousands of pulses per second to obtain contiguous coverage of the terrain at sub-meter spatial resolution, and with currently available technology, affords little scalability for significantly downsizing the hardware, or reducing the costs. A photon-counting ALSM sensor has been designed by the University of Florida and Sigma Space, Inc. for improved topographic mapping with lower power requirements and weight than traditional ALSM sensors. Major elements of the sensor design are presented along with preliminary simulation results. The simulator is being developed so that data phenomenology and target detection potential can be investigated before the system is completed. Early simulations suggest that precise estimates of terrain elevation and target detection will be possible with the sensor design.
The multiscale Kalman smoother (MKS) is a globally optimal estimator for fusing remotely sensed data. The MKS algorithm can be readily parallelized because it operates on a Markov tree data structure. However, such an implementation requires a large amount of memory to store the parameters and estimates at each scale in the tree. This becomes particularly problematic in applications where the observations have very different resolutions and the finest scale data are sparse or aggregated. Such cases commonly arise when fusing data to capture both regional and local structure. In this work, we develop an efficient MKS algorithm and apply it to the fusion of topographic and bathymetric elevation data.
Merging of point data acquired from ground-based and airborne scanning laser rangers has been demonstrated for cases in which a common set of targets can be readily located in both data sets. However, direct merging of point data was not generally possible if the two data sets did not share common targets. This is often the case for ranging measurements acquired in forest canopies, where airborne systems image the canopy crowns well, but receive a relatively sparse set of points from the ground and understory. Conversely, ground-based scans of the understory do not generally sample the upper canopy. An experiment was conducted to establish a viable procedure for acquiring and georeferencing laser ranging data underneath a forest canopy. Once georeferenced, the ground-based data points can be merged with airborne points even in cases where no natural targets are common to both data sets. Two ground-based laser scans are merged and georeferenced with a final absolute error in the target locations of less than 10cm. This is comparable to the accuracy of the georeferenced airborne data. Thus, merging of the georeferenced ground-based and airborne data should be feasible. The motivation for this investigation is to facilitate a thorough characterization of airborne laser ranging phenomenology over forested terrain as a function of vertical location in the canopy.
University of Florida (UF) researchers are developing an airborne laser swath mapping (ALSM) unit based on a paradigm referred to as photon counting ALSM, or PC-ALSM. In the PC-ALSM approach relatively low energy (few micro-joule) laser pulses are used to illuminate a surface 'patch' of terrain a few meters in extent. Reflected photons are imaged onto a multi-channel photomultiplier tube, to achieve high resolution (few decimeter) contiguous coverage of the terrain. A multi-channel multi-stop timing unit records both noise and signal events within a range gated window, and the noise events are filtered out of the data during post flight processing. A first generation PC-ALSM sensor, the Coastal Area Tactical-mapping System (CATS), is being developed with funding from the Office of Naval Research (ONR). The CATS sensor will be tested and operated from the UF Cessna 337 aircraft, which is equipped with a commercial ALSM unit. However, the ultimate goal of the UF research is to verify that PC-ALSM offers the possibility of developing a high resolution airborne laser mapping unit small and light enough, and with sufficient energy efficiency, to operate from a UAV.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.