Modeling and visualization of a complex hybrid system with different domains of energy flow and signal flow are described in this paper. It is a crane system situated in a barge complete with the load, electrical power, drive and control systems. A dynamically and functionally accurate model of the crane was developed. The implementation is in the freely available software suit of Virtual Test Bed (VTB) for simulation and Visual Extension Engine (VXE) for visualization. The bidirectional interaction of simulator and visualizer is fully utilized in this application. The further challenges confronted in implementing this particular system and any other complex system are discussed and possible solutions are suggested.

A technique is developed for synthesizing a high spectral resolution IR ship signature image, for use in an imaging IR Anti-Ship Cruise Missile (ASCM) model, from an IR scene database provided by the ship signature model NTCS/ShipIR. This synthesized IR ship image is generated for use over ranges representative of an ASCM engagement. The technique presented focuses on the application of in-band averaged transmittance to the source ship signature as a means of reducing the spectral calculations required by the cruise missile model. In order to achieve this reduction in computation, while preserving the fidelity of the apparent ship signature, the idea of sub-banding is introduced. Sub-banding describes the manner in which the IR band is partitioned into smaller bandwidths, such that the error produced in the ship's average contrast radiance due to the use of in-band averaged transmittance is minimized over range. The difference between the average contrast radiance of an IR ship image generated using in-band averaging and the average contrast radiance of a spectrally generated IR ship image is the metric for this minimization. This choice is based on measured data collected from the recent NATO SIMVEX trial, which used high quality IR measurements of the CFAV Quest in an effort to refine the NTCS/ShipIR model. The technique is general and applicable to any band(s) of interest. Results are presented which verify that the use of in-band averaged transmittance over an IR band (3.5-5.0 μm), partitioned using three optimal sub-bands, produces an IR ship image with an average contrast radiance within the desired error bar of a spectrally generated ship image's average contrast radiance.

A dynamic model of infrared missile engagements needs to integrate the output of signature models into a scene of given resolution with a changing viewpoint and moving targets against some background. Some signature prediction models are stand-alone software packages which currently cannot be dynamically interfaced to a running engagement model. They can be used to conveniently provide an image of an infrared target at high resolution at a single viewpoint. Using an imaging radiometer model, high-resolution, high-fidelity signatures can be quickly combined into a scene of desired configuration. This paper presents the derivation of such a model from physical and signal processing considerations, and its practical implementation. The derived methodology provides very high radiometric accuracy with a rigorously controlled error and smooth integration of objects moving through the scene.

Integrated high-fidelity physics-based simulations that include engagement models, image generation, electro-optical hardware models and control system algorithms have previously been developed by Boeing-SVS for various tracking and pointing systems. These simulations, however, had always used images with featureless or random backgrounds and simple target geometries. With the requirement to engage tactical ground targets in the presence of cluttered backgrounds, a new type of scene generation tool was required to fully evaluate system performance in this challenging environment. To answer this need, Irma was integrated into the existing suite of Boeing-SVS simulation tools, allowing scene generation capabilities with unprecedented realism. Irma is a US Air Force research tool used for high-resolution rendering and prediction of target and background signatures. The MATLAB/Simulink-based simulation achieves closed-loop tracking by running track algorithms on the Irma-generated images, processing the track errors through optical control algorithms, and moving simulated electro-optical elements. The geometry of these elements determines the sensor orientation with respect to the Irma database containing the three-dimensional background and target models. This orientation is dynamically passed to Irma through a Simulink S-function to generate the next image. This integrated simulation provides a test-bed for development and evaluation of tracking and control algorithms against representative images including complex background environments and realistic targets calibrated using field measurements.

Based on recent discoveries, we present a method to project a single structured pattern and then reconstruct the three-dimensional range from the distortions in the reflected and captured image. Traditional structured light methods require several different patterns to recover the depth, without ambiguity and albedo sensitivity, and are corrupted by object movement during the projection/capture process. Our method efficiently combines multiple patterns into a single composite pattern projection -- allowing for real-time implementations. Because structured light techniques require standard image capture and projection technology, unlike time of arrival techniques, they are relatively low cost. Attaining low cost 3D video acquisition would have a profound impact on most applications that are presently limited to 2D video imaging. Furthermore, it would enable many other applications. In particular, we are studying real time depth imagery for tracking hand motion and rotation as an interface to a virtual reality. Applications include remote controlled robotic interfacing in space, advanced cockpit controls and computer interfacing for the disabled.

High-resolution lidar data, acquired over a deciduous forest, were used to investigate the potential utility and limitations of current virtual reality (VR) software for remote sensing analysis and evaluation. Although a standard remote sensing software package provided a good overview of interpolated, smoothed lidar data, functionality was lower for gridded data that had not been interpolated. However, it was possible to drape orthophotographs and other images over the gridded lidar data, providing a useful method for investigating relationships between the lidar and other data sets. Using a commercial VR package, it was possible to view the original lidar point data; consequently, we were able to visualize the multiple returns from within the canopy of each tree. These point data were preferable for identifying surfaces within the data cloud, especially the ground surface, because the original point data allow an analyst to see, and work with, the full spatial complexity of the data. Being able to visualize the original data clouds also helps in delineating individual trees, the structure of returns within single trees and, potentially, to identify objects hidden within and beneath the trees. For a fully integrated remote-sensing VR package, additional functionality is needed to link point and interpolated coverages, and to enhance the interactive selection of data for further statistical analysis.

Thermal crossover is the phenomenon where the infrared signatures of two different objects in a scene are indistinguishable. A prediction method was developed where a series of infrared images is used as the basis to predict thermal crossover under different climatic conditions. Image recordings are made over the full diurnal cycle, for a fixed scene. We then develop a theoretical thermal model, describing dynamic temporal behaviour. Using the recorded images, the model parameters required to describe the temporal behaviour of the observed scene, are determined. The model, with the appropriate model parameters, is then used to create a new image sequence, predicting the scene appearance under different climatic conditions. The new image sequence is used to predict thermal crossover under the new set of climatic conditions. The paper closes with conclusions and recommendations for future work.

Edge contour extraction plays an important role in computer vision because edge contours are relatively invariant to the changes of illumination conditions, sensor characteristics, etc. In particular, edge contours can be used as matching primitives for correspondence determination, an important step in video geo-registration. In this paper, we present a new approach for edge contour extraction based on a three-step procedure that using a RCBS-based scheme, inherently more accurate results can be produced, even though the edge model used for edges is relatively simple. We also present recursive filters that can efficiently smooth splines by approximating a signal with a complete set of coefficients subject to certain regularization constraints. We demonstrate our method on both synthetic and real images.

An overview is given of the Home of the 21st Century Laboratory. The laboratory is operated as a joint program with America On-Line and George Washington University. The program is described with illustrations and discussion of the systems that are part of the laboratory. A Geospatial Information Systems (GIS) was developed as an integrating data management framework for activities and data management functions within the home. A variety of household information is collected and stored in multiple layers of information within the GIS system for easy access by members of the home. Technology options currently available for application in the home are described and assessed.

Airborne remote sensing has many applications that include vegetation detection, oceanography, marine biology, geographical information systems, and environmental coastal science analysis. Remotely sensed images, for example, can be used to study the aftermath of episodic events such as the hurricanes and floods that occur year round in the coastal bend area of Corpus Christi. This paper describes an Airborne Multi-Spectral Imaging System that uses digital cameras to provide high resolution at very high rates. The software is based on Delphi 5.0 and IC Imaging Control's ActiveX controls. Both time and the GPS coordinates are recorded. Three successful test flights have been conducted so far. The paper present flight test results and discusses the issues being addressed to fully develop the system.

We detected roads in aerial imagery using a method based on lineal feature detection. Our method used the products of wavelet coefficients at several scales were to identify and locate lineal features. Using our approach effectively increased the size of the region we examined when looking for possible road pixels, and decreased the probability of false positive road pixels. Then, we used a shortest path algorithm to link road pixels to form road networks. Our approach restricted possible road network solutions based on the initial detection of road pixels. We found that our approach leads to an effective method for detecting roads in aerial imagery. The method is general and can be applied to other features in imagery.

We have performed a study to identify optimal texture parameters for woodland segmentation in a highly non-homogeneous urban area from a temperate-zone panchromatic IKONOS image. Texture images are produced with the sum- and difference-histograms depend on two parameters: window size f and displacement step p. The four texture features yielding the best discrimination between classes are the mean, contrast, correlation and standard deviation. The f-p combinations 17-1, 17-2, 35-1 and 35-2 are those which give the best performance, with an average classification rate of 90%.

Features of radar signals reflection by quasiperiodic surface in decameter range of wavelengths are investigated. As a probe signal is used complex signal, matched with a surface frequency coefficient of reflection. The form of radioecho is analyzed. Simulation of radar signals back-scattering from the marine surge surface is conducted. Differences in outcomes of simulation and analytical solutions are considered.

Hough transform theory provides a heuristically appealing approach toward finding lineal features in imagery. Unfortunately direct algorithmic implementation of its theory results in many practical problems. We provide two interlocking theoretical extensions to greatly enhances the Hough transform's ability to handle finite lineal features and allow directed search for parallel lines within the scene while balancing memory and computational complexity. Both extensions involve expansion of the Hough space concept to allow easier access to processed data for both dedicated silicon and general-purpose computer implementations.

The Squeezed Signature Analysis (SSA) hyperspectral classification method is presented as a fast method to compare the target spectral signature to all the signatures in the spectral library. This discussion includes the possible use of this technique on-board a hypothetical remote sensing system that could take advantage of parallel computations.

A new algorithm for multichannel radar image mutual superimposing (registration) and geometric correction is proposed. It uses ground control points (GCPs) selected manually or automatically for warp and base images and the branch-and-bound technique for initial estimation of geometric transform parameters. We show that due to taking into account the GCP selection errors for both warp and base images, the proposed modification of branch-and-bound algorithm allows to additionally improve the accuracy of warp parameter estimation. To reduce the computational complexity of branch-and-bound algorithm an approach based on optimal selection of center position for warp image coordinate system is proposed. It is shown that the coordinate system shifting towards the center of warp image GCP bounding box permits to accelerate the search process. For additional speeding up the branch-and-bound algorithm, bounded alignment approach is applied. For establishing the correspondence between GCPs and for detection of erroneous points, a novel method of statistical analysis of GCP superimposing errors is put forward. The designed algorithm is tested for both simulated and real data demonstrates high efficiency and robustness.

A wide variety of hyper-spectral (HS) sensors and collection platforms are in existence. This paper investigates hyper-spectral imaging systems (HIS) worldwide in order to address issues associated with the better both airborne and space based systems are included in the review. Examples of the sensors include ENVISAT, SCIAMACHY, TERRA, AQUA, MOPITT, MIPAS, AVIRIS, LIDAR, Landsat 7 and others. Applications include geo-environmental studies, aerosol release, materials identification, agricultural studies, atmospheric studies, and many others. Two case studies are presented that address the evaluation of African smog and its effect on the African ecosystem and the evaluation of aerosol pollution in the northeastern region of the United states with particular attention to particulate matter.

Hyperspectral imagining has been recently been used to obtain several water quality parameters in water bodies either inland or in oceans. Optical and thermal have proven that spatial and temporal information needed to track and understand trend changes for these water quality parameters will result in developing better management practices for improving water quality of water bodies. This paper will review water quality parameters Chlorophyll (Chl), Dissolved Organic Carbon (DOC), and Total Suspended Solids (TSS) obtained for the Sakonnet River in Narragansett Bay, Rhode Island using the AVIRIS Sensor. The AVIRIS Sensor should improve the assessment and the definition of locations and pollutant concentrations of point and non-point sources. It will provide for necessary monitoring data to follow the clean up efforts and locate the necessary water and wastewater infrastructure to eliminate these point and non-point sources. This hyperspectral application would enhance the evaluation by both point and non-point sources, improve upon and partially replace expenses, labor intensive field sampling, and allow for economical sampling and mapping of large geographical areas.

Precision farming relies on the cost effectiveness of collecting and interpreting data, which describes the variations of agricultural conditions such as crop stresses, nutrient deficiencies, water stresses, or pest infestation. Hyperspectral remote sensing from satellites and airborne sensors can be a way to obtain data needed to develop site-specific farming management strategies. The primary objective of the hyperspectral applications in precision farming is to provide farmers with a technology, which can detect specific crop conditions that can be used to program variable-rate applications. Applications of water, pesticides, and fertilizer can be tailored to the needs of the agricultural crops, based on the conditions reflected on the imagery. This paper presents an experimental study performed in Beltsville, Maryland for assessing the plant density and nutrient uptake of corn using a simple photographic method from a model airplane versus obtaining hyperspectral imagery from an airborne sensor. The hyperspectral sensor utilized in this study was the AISA sensor. These remote sensors can measure the temperature of plants; or to be more specific, they can measure how much energy plants emit at the visible and near-infrared wavelengths of the spectrum, such as water and vegetation.

Despite the considerable slowdown of wetlands loss in conterminous U.S., management of these valuable resources continues to be an area of interest for environmental professionals. The development of remote sensing technologies, particularly hyperspectral, offers an alternative for ecological and functional assessment of these sites. Extensive hyperspectral data image collected from the various sensor types can be analyzed by discriminatory techniques for reflectance analysis. Although data processing can become tedious, it enables scientists to target the various inherited characteristics of large wetland areas such as vegetative species and habitats. This information can be applied to determine the health and functionality of the nation's wetlands for means of wetland characterization, assessment, management and possible restorative efforts to bring a consistent and fundamental change on how these are managed today.

Public Works facilities require up-to-date information on the health status of the road network they maintain. However, roadway maintenance and rehabilitation involves the greatest portion of a municipality's annual operating budget. Government officials use various technologies such as a pavement management system to assist in making better decisions about their roadways systems, pavement condition, history, and projects. Traditionally, manual surveying has served as the method of obtaining this information. To better assist in decision-making, a regionally specific spectral library for urban areas is being developed and used in conjunction with hyperspecrtal imaging, to map urban materials and pavement conditions. A Geographical Information and Positioning System (GIS/GPS) will also be implemented to overlay relative locations. This paper will examine the benefits of using hyperspectral imaging over traditional methods of roadway maintenance and rehabilitation for pavement management applications. In doing so, we will identify spatial and spectral requirements for successful large-scale road feature extraction.

This paper analyzes the feasibility and performance of HSI systems for medical diagnosis as well as for food safety. Illness prevention and early disease detection are key elements for maintaining good health. Health care practitioners worldwide rely on innovative electronic devices to accurately identify disease. Hyperspectral imaging (HSI) is an emerging technique that may provide a less invasive procedure than conventional diagnostic imaging. By analyzing reflected and fluorescent light applied to the human body, a HSI system serves as a diagnostic tool as well as a method for evaluating the effectiveness of applied therapies. The safe supply and production of food is also of paramount importance to public health illness prevention. Although this paper will focus on imaging and spectroscopy in food inspection procedures -- the detection of contaminated food sources -- to ensure food quality, HSI also shows promise in detecting pesticide levels in food production (agriculture.)

A wide variety of hyper-spectral (HS) sensors and collection platforms are in existence. This paper investigates hyper-spectral imaging systems (HIS) worldwide in order to compose a comprehensive listing of these systems. A meta-data structure was developed to identify basic parameter information for all sensors that were reviewed. Systems were grouped into two primary categories of space borne and air borne. Sensors were further grouped into three types of imaging spectrometers; whiskbroom line array band interleaved by pixel, push broom area array band interleaved by line and framing camera band sequential methodology. Several sensor systems are presented using the meta-data structure and parameters developed for the analysis. A summary table identifying all sensor systems that were evaluated is presented. Applications include geo-environmental studies, aerosol release, materials identification, agricultural studies, atmospheric studies, and many others.

Oil pollution is a very important aspect in the environmental field. Oil pollution is an important subject due to its capacity to adversely affect animals, aquatic life, vegetation and drinking water. The movement of open water oil spills can be affected by mind, waves and tides. Land based oil spills are often affected by rain and temperature. It is important to have an accurate management of the cleanup. Remote sensing and in particular hyper-spectral capabilities, are being use to identify oil spills and prevent worse problems. In addition to this capability, this technology can be used for federal and state compliance of petroleum related companies. There are several hyper-spectral sensors used in the identification of oil spills. One commonly use sensor is the Airborne Imaging Spectroradiometer for Applications (AISA). The main concern associated with the use of these sensors is the potential for false identification of oil spills. The use of AISA to identify an oil spill over the Patuxent River is an example of how this tool can assist with investigating an oil pipeline accident, and its potential to affect the surrounding environment. A scenario like this also serves as a good test of the accuracy with which spills may be identified using new airborne sensors.

A data organization, scalable structure, and multiresolution visualization approach is described for precision markup modeling in a global geospatial environment. The global environment supports interactive visual navigation from global overviews to details on the ground at the resolution of inches or less. This is a difference in scale of 10 orders of magnitude or more. To efficiently handle details over this range of scales while providing accurate placement of objects, a set of nested coordinate systems is used, which always refers, through a series of transformations, to the fundamental world coordinate system (with its origin at the center of the earth). This coordinate structure supports multi-resolution models of imagery, terrain, vector data, buildings, moving objects, and other geospatial data. Thus objects that are static or moving on the terrain can be displayed without inaccurate positioning or jumping due to coordinate round-off. Examples of high resolution images, 3D objects, and terrain-following annotations are shown.