Maura C. Lohrenz

A Model of Clutter for Complex, Multivariate Geospatial Displays

Objective: A novel model of measuring clutter in complex geospatial displays was compared with human ratings of subjective clutter as a measure of convergent validity. The new model is called the color-clustering clutter (C3) model. Background: Clutter is a known problem in displays of complex data and has been shown to affect target search performance. Previous clutter models are discussed and compared with the C3 model. Method: Two experiments were performed. In Experiment 1, participants performed subjective clutter ratings on six classes of information visualizations. Empirical results were used to set two free parameters in the model. In Experiment 2, participants performed subjective clutter ratings on aeronautical charts. Both experiments compared and correlated empirical data to model predictions. Results: The first experiment resulted in a .76 correlation between ratings and C3. The second experiment resulted in a .86 correlation, significantly better than results from a model developed by Rosenholtz et al. Outliers to our correlation suggest further improvements to C3. Conclusions: We suggest that (a) the C3 model is a good predictor of subjective impressions of clutter in geospatial displays, (b) geospatial clutter is a function of color density and saliency (primary C3 components), and (c) pattern analysis techniques could further improve C3. Application: The C3 model could be used to improve the design of electronic geospatial displays by suggesting when a display will be too cluttered for its intended audience.

Vector map data compression with wavelets

Abstract : Wavelets and wavelet transforms can be used for vector-map data compression. The choice of wavelet, the level of decomposition, the method of thresholding, the height of the threshold, relative CPU times and file sizes, and reconstructed map appearance were investigated using the Wavelet Toolbox of MATLAB. Quantitative error measures were obtained. For two test vector-map data sets consisting of longitude and latitude points, compressions of 35 to 50 percent (1.5:1 to 2:1) were obtained with root-mean-square errors less than 0-003 to 0-01 deg longitude/latitude for wavelet packet decompositions using selected wavelets.

The Automated Change Detection and Classification Real-time (ACDC-RT) System

This paper presents an Automated Change Detection and Classification (ACDC) System, developed by the Naval Research Laboratory (NRL) and the Naval Oceanographic Office (NAVOCEANO), which aids analysts in performing change detection in real-time (RT) by co-registering new and historical imagery and using automated change detection algorithms that suggest imagery changes. In this paper, ACDC-RT components are described and results given from a recent change detection experiment.

Human Factors Issues in Advanced Moving-map Systems

Vector-based maps are an advanced capability of digital moving-map systems that are easily customised and can be powerful aids to aircrew information processing and decision-making, However, they may place excessive demands on an aircrews information processing requirements, cause an increase in workload, and degrade situational awareness if the user interface is not designed properly. There is little information available about the human factors and situational awareness issues relevant to vector-based maps. In this paper, we summarise relevant research on human factors and situational awareness aspects of using vector-based maps identify key issues, anti recommend directions for future research.

Pilot Preferences on Vector Moving-Map Displays

Vector map databases offer the potential for customised cockpit moving-map displays, in which user-specified cartographic features can be layered to meet mission requirements. The disadvantage of vector moving-maps is the potential for increased user workload, In 1995. the Naval Research Laboratory and the Naval Air Weapons Center jointly performed a preference study, during which aircrew viewed demonstrations of prototype moving-map displays and responded to a detailed questionnaire concerning the usefulness of each display This paper summarises aircrew interviews from that study pertaining to both vector moving-map displays and vector feature overlays, including Height-Above-Threshold (HAT), threat rings, and Clear Line-of-Sight (CLOS).

Intelligent Electronic Navigational Aids: A New Approach

Intelligent devices, with smart clutter management capabilities, can enhance a users situational awareness under adverse conditions. Two approaches to assist a user with target detection and clutter analysis are presented, and suggestions on how these tools could be integrated with an electronic chart system are further detailed. The first tool, which can assist a user in finding a target partially obscured by display clutter, is a multiple-view generalization of AdaBoost. The second technique determines a meaningful measure of clutter in electronic displays by clustering features in both geospatial and color space. The clutter metric correlates with preliminary, subjective, clutter ratings. The user can be warned if display clutter is a potential hazard to performance. Synthetic and real data sets are used for performance evaluation of the proposed technique compared with recent classifier fusion strategies

Classifying sidescan sonar images using self organizing maps

Self organizing maps (SOMs) can be used for computer‐aided classification of objects found in two‐dimensional snippets of sidescan sonar images. SOMs are briefly discussed, including the choice of features or attributes as well as various types of input data. The inputs can be, for example, the data values themselves, either raw or processed images; the amplitudes of the Fourier transform coefficients of the data; the wavelet transform coefficients of the data; the energies of the horizontal, vertical, or diagonal wavelet coefficients; the autocorrelation of the data; the Hartley transform coefficients of the data; the cepstrum; the dimensions of the object; or the sonar bright spot and shadow character. Tabular results and two‐dimensional maps showing the groupings of measured and processed sidescan data are presented. Comparisons are made with human classifications of the same images. [Research supported in part by NRL‐ASEE Summer Faculty Research Program.]

Automated change detection with area matching

When resurveying a geographic area of the seafloor during sidescan change detection operations, an automated method to match bottom objects imaged previously with objects imaged in the resurvey can increase efficiency and accuracy. The geographic position of a new object relative to a historical object is a good indicator of a match. However, due to position error within either survey, there may be more than one spatially‐close object in the new imagery. To complicate matters further, the reflected energy from the new object may be significantly different given a different incidence angle in the resurvey or the partial burial of the object. In addition, the resurveyed object image may be below the threshold set for automatic recognition and falsely eliminated. This presentation will address these problems and suggest possible methods for matching “constellations” of bottom objects by Dijkstras minimum cost ‐ maximum flow algorithm, control point matching, and the data‐association procedure.

Clutter prediction using artificial neural networks.

Clutter is a known problem in electronic geospatial (map) displays, on which many different types of data can be combined and presented as a single image. In this context, clutter may be thought of as an overabundance of information, which reduces display usability by the viewer. To declutter a geospatial display, it is necessary to first predict the amount of clutter a human observer might perceive. Computer‐aided classification of maps according to the amount of clutter likely to be perceived by a human viewer is the goal of the research described here. Artificial neural networks are among the possible choices of computing techniques that can be used for this task. The network is trained using prior clutter classifications of training maps made by humans, so that automated prediction of clutter for a new map is possible. The objective is to have the network classify maps according to clutter as perceived by human judges. Several neural network algorithms and trials with data consisting of cluttered map classifications and response times by human judges are described. The choice of input features, including the use of principal components, is discussed. Preliminary results with test maps show good prediction of clutter.

New Trends in Intelligent Electronic Navigational Aids

The smart management of clutter is a key component in designing intelligent, next-generation user interfaces and electronic displays. Intelligent devices can enhance a users situational awareness under adverse conditions. In this paper we present two approaches to assist a user with target detection and clutter analysis, and we suggest how these tools could be integrated with an electronic chart system. The first tool, an information fusion technique, is a multiple-view generalization of AdaBoost, which can assist a user in finding a target partially obscured by display clutter. The second technique clusters geospatial features on an electronic display and determines a meaningful measure of display clutter. The clutter metric correlates with preliminary, subjective, clutter rankings. The metric can be used to warn a user if display clutter is a potential hazard for his performance. We compare the performance of the proposed techniques with recent classifier fusion strategies on a set of synthetic data.