K. Clint Slatton

Geodetic laser scanning

Producing surface maps at submeter resolution, even over heavily forested terrain, GLS can reveal the fine structure of such features as faults, landslides, and drainage patterns.

On the potential for high-resolution lidar to improve rainfall interception estimates in forest ecosystems

Closing the gaps in the water budget of forested ecosystems is a first-order challenge, with immediate implications for regional water supply, ecosystem function, and landscape biogeochemistry. Rainfall interception by vegetated canopies can be as great as 50% of total rainfall. There is considerable uncertainty in predicting this ecosystem property, which makes it one of the primary constraints in spatial water budgeting. Interception is largely controlled by vertical structure and canopy gaps on a relatively small scale. Emerging remote sensing technologies, such as lidar (light detection and ranging), now offer an unprecedented opportunity to quantify canopy architecture in three dimensions across the landscape. Use of such high-resolution spatial data, along with improved rainfall interception models, will aid in ecosystem process studies and the development of tools and incentives that could influence land-use policy and decision making in the future.

A Parametric Model for Characterizing Seabed Textures in Synthetic Aperture Sonar Images

High-resolution synthetic aperture sonar (SAS) systems yield finely detailed images of sea bottom environments. SAS image texture models must be capable of representing a wide variety of sea bottom environments including sand ripples, coral or rock formations, and flat hardpack. In this paper, a parameterized model for SAS image textures is derived from the autocorrelation functions (ACFs) of the SAS imaging point spread function (PSF) and the ACF of the seabed texture sonar cross section (SCS). The proposed texture mixture model is analytically tractable and parameterized by component mixing parameters, mixture component correlation lengths, the single-point intensity image statistical shape parameter, and the rotation of the ACF mixture components in the 2-D imaging plane. An iterative parameter estimation algorithm based on the expectation-maximization (EM) algorithm for truncated data is presented and tested against various synthetic and real SAS image textures. The performance of the algorithm is compared and discussed for synthetically generated data across various image sizes and texture characteristics. The model fit is also compared against a small set of real SAS survey images and is shown to accurately fit the imaging PSF and seabed SCS ACF for these textures of interest.

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.

A parameterized statistical sonar image texture model

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.

A simulator for airborne laser swath mapping via photon counting

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.

Hurricane Response of Nearshore Borrow Pits from Airborne Bathymetric Lidar

Airborne bathymetric lidar surveys taken in Florida before and after the severe 2004 and 2005 hurricane seasons show infilling of seventeen dredged nearshore borrow pits. During these seasons, groups of pits captured volumes that were the equivalent of up to four years of net longshore transport, even though only one of the seventeen pits studied was inside the presumed depth of closure. Unsurprisingly, dimensionless infilling increased strongly with the ratio of wave height to pit depth. For open coast pits with large alongshore lengths, cross-shore infilling appeared to dominate over longshore infilling but both processes may be of comparable importance in shorter pits. Infilling of three borrow pits adjacent to ebb shoals was found to be considerably larger than on open coasts. Bathymetric changes in borrow pits occurred at greater depths than on nearby undisturbed profiles. Crude estimates of the long term infilling rates from tropical cyclones indicate that annual infilling volumes may be equivalent to more than one quarter of the expected net longshore transport at some locations. However, the episodic nature of hurricanes means that infilling events will be highly irregular.

A Scalable Approach to Fusing Spatiotemporal Data to Estimate Streamflow via a Bayesian Network

Flow of water through stream networks directly impacts flooding and transport of sediments and pollutants in watershed systems. Hence, knowledge of streamflow is critical for water management and mitigation of flooding and drought events. Unfortunately, spatially dense networks of in situ streamflow measurements are generally unavailable and would be prohibitively expensive to deploy and maintain. Thus, a data fusion framework is needed that utilizes available data to predict streamflow. Observed data in spatial (e.g., topography and land cover), temporal (e.g., streamflow and groundwater levels), and spatiotemporal domains (e.g., rainfall) impact streamflow. Some of these quantities can be obtained from remote sensing imagery; however, combining such disparate data types using traditional data fusion methods is problematic. Physically based hydrologic models have been used to predict streamflow but often with significant uncertainty because numerous assumptions are made for many unmeasured input and parameter values. Traditional Bayesian inference approaches suffer from superlinear increases in computational complexity as the number of data sets to be fused grows. In this paper, a scalable spatiotemporal approach based on Bayesian networks (BNs) is presented for estimating streamflow. An information-theoretic methodology based on conditional entropy is employed to quantify the impact of adding nodes in the BN in terms of information gained. The framework offers the flexibility of embedding knowledge from hydrologic models calibrated for the study area by introducing them as additional nodes in the network, thereby improving prediction accuracy. Posterior probabilities of estimates and the associated entropy provide valuable information on the quality of predictions and also offer directions for future watershed instrumentation.

Correntropy based matched filtering for classification in sidescan sonar imagery

This paper presents an automated way of classifying mines in sidescan sonar imagery. A nonlinear extension to the matched filter is introduced using a new metric called correntropy. This method features high order moments in the decision statistic showing improvements in classification especially in the presence of noise. Templates have been designed using prior knowledge about the objects in the dataset. During classification, these templates are linearly transformed to accommodate for the shape variability in the observation. The template resulting in the largest correntropy cost function is chosen as the object category. The method is tested on real sonar images producing promising results considering the low number of images required to design the templates.

Dynamic factor graphs: A novel framework for multiple features data fusion

The Dynamic Tree [1] (DT) Bayesian Network is a powerful analytical tool for image segmentation and object segmentation tasks. Its hierarchical nature makes it possible to analyze and incorporate information from different scales, which is desirable in many applications. Having a flexible structure enables model selection, concurrent with parameter inference. In this paper, we propose a novel framework, dynamic factor graphs (DFG), where data segmentation and fusion tasks are combined in the same framework. Factor graphs (FGs) enable us to have a broader range of modeling applications than Bayesian networks (BNs) since FGs include both directed acyclic and undirected graphs in the same setting. The example in this paper will focus on segmentation and fusion of 2D image features with a linear Gaussian model assumption.